introduction to artificial intelligence peer graded assignment

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Introduction to Artificial Intelligence (AI)

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• What is AI? Applications and Examples of AI
• This week, you will learn what AI is. You will understand its applications and use cases and how it is transforming our lives.
• AI Concepts, Terminology, and Application Areas
• This week, you will learn about basic AI concepts. You will understand how AI learns, and what some of its applications are.
• AI: Issues, Concerns and Ethical Considerations
• AI is everywhere, transforming the way we live, work, and interact — that's why it's so important to build and use it in line with ethical expectations. This week, you will learn about issues and concerns surrounding AI, as well as how AI ethics helps practitioners build and use AI responsibly. This information will help you understand AI's potential impacts on society so that you can have an informed discussion about its risks and benefits.
• The Future with AI, and AI in Action
• This week, you will learn about the current thinking on the future with AI, as well as hear from experts about their advice to learn and start a career in AI. You will also demonstrate AI in action by utilizing Computer Vision to classify images.
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• SK Shakuntala Kumari 4 years ago Artificial intelligence is a appropriate course and this course is very easy to understand.it helps other listeners and discover great course.this course is very interesting. Helpful

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Introduction to Artificial Intelligence (AI) – Quiz Answers

Ointroduction to artificial intelligence (ai) – quiz answers.

In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini project. This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not.

Graded: What is AI? Applications and Examples of AI

1. Which of the following is NOT a good way to define AI?

Graded: AI Concepts, Terminology, and Application Areas

4. Which of the following is NOT an attribute of Unsupervised Learning?

7. When creating deep learning algorithms, developers configure the number of layers and the type of functions that connect the outputs of each layer to the inputs of the next.

9. Which of these activities is not required in order for a neural network to synthesize human voice?

Graded: AI Issues, Ethics and Bias

Final assignment part one, 1. how would you define ai.

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

2. Explain an application or use-case of AI that fascinates YOU .

3. pick a specific industry or an aspect of our lives or society and describe how you think it will be impacted by artificial intelligence in future ..

Artificial Intelligence in Education: It must be very tedious for a teacher to grade homework and tests for large lecture courses. A significant amount of time is consumed to interact with students, to prepare for class, or work on professional development. But, this will not be the case anymore. Though it can never replace human work, it is pretty close to it. So, with the automated grading system checking multiple-choice questions, fill-in-the-blank testing and automated grading of students can be done in a jiffy. It can tell the areas, where there is a need for improvement –

A lot of times, it happens that the teachers may not be aware of the gaps that a student might face in the lectures and educational materials. This can leave students confused about certain concepts. With AI, the system alerts the teacher and tell what is wrong. It gives students a customized message which offers hints to the correct answer.

Peer-graded Assignment: Final Assignment Part Two

Upload the screenshot that you saved in Exercise 4, Task 3 of the previous hands-on exercise titled “Classify your images with AI”. Ensure the screenshot includes the picture you uploaded along the labels and confidence scores below it.

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CS47100: Introduction to Artificial Intelligence (Fall 2023)

Course Information

Artificial intelligence (AI) is about building intelligent machines that can perceive and act rationally to achieve their goals. To prepare students for this endeavor, we cover the following topics in this course: Search, constraint satisfaction, logic, reasoning under uncertainty, machine learning, and planning. There will be four assignments in the form of both written and programming problems.

Pre-requisites:

• CS251 Data Structures (grade of C or better)
• [AIMA] S. Russell and P. Norvig (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th Edition. (ISBN:9780134610993)
• You can also use the 3rd edition and find the corresponding sections to read.
• Assignments: 40% (10% each)
• Midterm: 30%
• Final Exam: 30%
• Lecture slides and recordings will be posted on Brightspace.
• The instructor & TAs can be best reached through Ed Discussion. Please post your questions there instead of emailing TAs.
• During office hours or on Ed Discussion, please avoid posting partial homework solutions or asking TAs to "review" your code/solution.
• Tutorial for learning Latex with Overleaf: [Link]

Instructor & TAs

Raymond a. yeh.

Email: rayyeh [at] purdue.edu Office Hour: Mon. 4:30PM-5:30PM Location: Zoom (See Ed.)

Email: du286 [at] purdue.edu Office Hour: Thur. 4PM-5PM Location: HAAS 143

Mehmet Oguz Sakaoglu

Email: msakaogl [at] purdue.edu Office Hour: Friday 10AM-11AM Location: HAAS G072

Chiao An Yang

Email: yang2300 [at] purdue.edu Office Hour: Tuesday 3PM-4PM Location: HAAS G072

Hairong Yin

Email: yin178 [at] purdue.edu Office Hour: Thursday 2PM-3PM Location: HAAS 143

Haomeng Zhang

Email: zhan5050 [at] purdue.edu Office Hour: Friday 1:30PM-2:30PM Location: HAAS 143

Nathan Reed

Email: nnreed [at] purdue.edu Office Hour: Friday 6PM-7PM Location: HAAS 143

Ananya Singh

Email: singh745 [at] purdue.edu Office Hour: TBD Location: TBD

Time & Location

• Time: Tuesday & Thursday (6:00 pm - 7:15 pm)
• Location: Wilmeth Active Learning Center (WALC) 1018

Other Resource

• BrightSpace
• Ed Discussion

Course Schedule

The following schedule is tentative and subject to change.

Date	Event	Description	Readings
Aug 22	Lecture 1	Introduction & Overview	AIMA Ch. 1
Aug 24	Lecture 2	AI Representation	AIMA Ch. 2
Aug 28	Info.	Assignment 1 released
Aug 29	Lecture 3	Search - I: Problem Formulation	AIMA Ch. 3.1-3.3
Aug 31	Lecture 4	Search - II: Uninformed Search	AIMA Ch. 3.4
Sep 5	Lecture 5	Search - III: Informed search	AIMA Ch. 3.5-3.6
Sep 7	Lecture 6	Local search	AIMA Ch. 4.1
Sep 12	Lecture 7	Adversarial search - I: Minimax	AIMA Ch. 5.1-5.2
Sep 14	Lecture 8	Adversarial search - II: Alpha-Beta Pruning	AIMA Ch. 5.3
Sep 15	Deadline	Assignment 1 due (Friday Sep 15, 11:59PM)
Sep 18	Info.	Assignment 2 released
Sep 19	Lecture 9	CSP - I: Problem Formulation and Inference	AIMA Ch. 6.1-6.2
Sep 21	Lecture 10	CSP - II: Backtracking and Local Search	AIMA Ch. 6.3-6.5
Sep 26	Lecture 11	Logic - I: Propositional Logic	AIMA Ch. 7.2-7.4
Sep 28	Lecture 12	Logic - II: Propositional Theorem Proving	AIMA Ch. 7.5-7.6
Oct 3	Lecture 13	Logic - III: First Order Logic Senmatics	AIMA Ch. 8.2-8.3
Oct 5	Lecture 14	Logic - IV: First Order Logic Inference	AIMA Ch. 9.1-9.5
Oct 10	Info.	Fall Break
Oct 12	Lecture 15	Probability and Uncertainty	AIMA Ch. 12.2-12.6
Oct 13	Deadline	Assignment 2 due (Friday Oct 13, 11:59PM)
Oct 17	Lecture 16	Midterm Review
Oct 19	---	No class (Evening midterm exam)
Oct 19	Exam	Evening midterm exam (8:00PM - 10:00PM)
Oct 23	Info.	Assignment 3 released
Oct 24	Lecture 17	Bayesian Networks - I: Representation and Semantics	AIMA Ch. 13.1-13.2
Oct 26	Lecture 18	Bayesian Networks - II: Independence
Oct 31	Lecture 19	Bayesian Networks - III: Inference	AIMA Ch. 13.3-13.4
Nov 2	Lecture 20	Markov Decision Process - I: Problem Formulation	AIMA Ch. 17.1
Nov 7	Lecture 21	Markov Decision Process - II: Value Iteration	AIMA Ch. 17.2.1
Nov 9	Lecture 22	Markov Decision Process - III: Policy Iteration	AIMA Ch. 17.2.2
Nov 10	Deadline	Assignment 3 due (Friday Nov. 10, 11:59PM)
Nov 13	Info.	Assignment 4 released
Nov 14	Lecture 23	Reinforcement Learning - I: Problem Formulation	AIMA Ch. 22.1-22.2
Nov 16	Lecture 24	Reinforcement Learning - II: Q-Learning	AIMA Ch. 22.3
Nov 21	Lecture 25	Supervised Learning - I: Overview	AIMA Ch. 19.1-19.2
Nov 23	Info.	No class (Thanksgiving Break)
Nov 28	Lecture 26	Supervised Learning - II: Model Search and Evaluation	AIMA Ch. 19.4
Nov 30	Lecture 27	Supervised Learning - III: Deep Learning	AIMA Ch. 21.1
Dec 1	Deadline	Assignment 4 due (Friday Dec 1, 11:59PM)
Dec 5	Lecture 28	Computer Vision	AIMA Ch. 25
Dec 7	Lecture 29	Final Review
Dec 14	Exam	Final Exam (7:00PM-9:00PM)

Late & Absence Policy

A 10% penalty will be applied (per day) to late assignments. Assignments that are more than two days late will not be accepted. For the consistency and fairness to all students, we follow the policy and absence request through the Office of the Dean of Students.

Academic Honesty

Please refer to Purdue's Student Guide for Academic Integrity . Academic dishonesty will result in an automatic zero on an assignment and your course grade will be reduced by one full letter grade. A second attempt will result in a failing grade for the course. It is one's responsibility to prevent others from copying your work.

Accessibility

Purdue University strives to make learning experiences as accessible as possible. If you anticipate or experience physical or academic barriers based on disability, please contact the Disability Resource Center at: [email protected] or by phone at 765-494-1247 and the course instructor to arrange for accommodations.

Classroom Guidance Regarding Protect Purdue

Any student who has substantial reason to believe that another person is threatening the safety of others by not complying with Protect Purdue protocols is encouraged to report the behavior to and discuss the next steps with their instructor. Students also have the option of reporting the behavior to the Office of the Student Rights and Responsibilities . See also Purdue University Bill of Student Rights and the Violent Behavior Policy under University Resources in Brightspace.

University Policies

Please refer to additional university policies in BrightSpace .

CS540 Introduction to Artificial Intelligence

CS540, Spring 2022 Department of Computer Sciences University of Wisconsin–Madison

Introduction to Artificial Intelligence

CS540, Spring 2021 Department of Computer Sciences University of Wisconsin–Madison

Lastest Announcements

If you are on the waiting list , you may want to consider the following options: Wait until the first week of classes as we will admit more students from the waitlist once we see how many registered students drop the course. Take the class when it is offered again in another semester. Please contact CS Enrollment Office ([email protected]) for any enrollment related question.

Course Information

Course learning outcomes: Students gain principles of knowledge-based search techniques; automatic deduction, knowledge representation using predicate logic, machine learning, probabilistic reasoning. Students develop applications in tasks such as problem solving, data mining, game playing, natural language understanding, and robotics.

Number of credits associated with the course: 3

How credit hours are met by the course: This class meets for two 75-minute class periods each week over the semester and carries the expectation that students will work on course learning activities (reading, writing, problem sets, studying, etc) for about 3 hours out of classroom for every class period. The syllabus includes more information about meeting times and expectations for student work.

Prerequisite: (COMP SCI 300 or 367) and (MATH 211, 217, 221, or 275) or graduate/professional standing or declared in the Capstone Certificate in Computer Sciences for Professionals.

• Section 001: TR 9:30-10:45am, Social Science 6203
• Section 002: TR 2:30-3:45pm, Chem S413
• Section 003: TR 11-12:15, Birge 145

Textbook : Artificial Intelligence: A Modern Approach (4th edition). Stuart Russell and Peter Norvig. Pearson, 2020. ISBN 978-0134610993. (textbook is optional, but may be useful)

Course Objectives

• Understand and be able to apply the foundational tools in Machine Learning and Artificial Intelligence : Linear algebra, Probability, Logic, and elements of Statistics.
• Understand core techniques in Natural Language Processing (NLP) , including bag-of-words, tf-idf, n-Gram Models, and Smoothing.
• Understand the basics of Machine Learning . Identify and summarize important features in supervised learning and unsupervised learning.
• Distinguish between regression and classification, and understand basic algorithms: Linear Regression, k-Nearest Neighbors, and Naive Bayes.
• Understand the basics of Neural Networks : Network Architecture, Training, Backpropagation, Stochastic Gradient Descent.
• Learn aspects of Deep Learning , including network architectures, convolution, training techniques.
• Understand the fundamentals of Game Theory .
• Understand how to formulate and solve several types of Search problems .
• Understand basic elements of Reinforcement Learning .
• Consider how Artificial Intelligence and Machine Learning problems are applied in Real - World settings and the Ethics of Artificial Intelligence.

Lecture Delivery

In the regular lecture time, we will have synchronous classes, during which the instructors will lecture, the class will enagage in Q&A, ungraded quizzes, and discussions.

During lecture hours: Each lecture will be a series of short mini-lectures. The lecture will be divided into three blocks. In each block, the instructor will lecture, pause for interactive Q&A, and deliver short quiz questions to clear up any confusion before proceeding to the next block. We would like, whenever possible, all students to participate in the quiz.

We will use Piazza for Q&A. Please follow these rules:

• Please check if someone has posted the same / similar question before you; it’s much easier if we build on the thread.
• Use an informative Summary line to help others.

In summary : In class you will attend real-time mini-lectures by the instructor, ask / discuss questions, and take short quizzes for student understanding.

The following weights are used:

• Midterm Exam : 15%
• Final Exam : 15%
• Homework Assignments : 70%

At the end of the semester, the final letter grades are given based on an approximate curve. The weights placed on the assignments will be strictly enforced.

The final letter grade will be assigned based on the percentile of the averaged points in the class:

• A : Top 15-25% of course grades
• AB : next 15-25%
• B : next 0-20%
• BC : next 0-20%
• C : next 0-20%

As student performance may vary from semester to semester, the instructors reserve the right to adjust this distribution. McBurney Center students should contact the instructors to specify any special requests for the exams or homework assignments together with the supporting documentation provided by the McBurney Center. We will do our best to accommodate the requests.

Homework Policies

Homework assignments include written problems and programming (in Python). Frequently-asked questions (FAQs) on homework assignments will be posted on Piazza . Unless otherwise specified, homework is always due Tuesday morning at 9am . Late submissions will not be accepted. Assignment grading questions must be raised with the TAs within 72 hours after it is returned. Note that a regrading request for a part of a homework question may trigger the grader to regrade the entire homework and could potentially take points off. Regrading will be done on the original submitted work, no changes allowed.

TWO lowest homework scores are dropped from the final homework average calculation. This drop is meant for emergency usage. Additional drops, late days, or homework extensions will not be provided. We encourage you to use a study group for doing your homework. Students are expected to help each other out, and if desired, form ad-hoc homework groups. However, each student must produce and turn in their own, unique work.

Campus Spaces for Virtual Learning & Testing : Dedicated on-campus spaces with high-speed internet are available for students to reserve for any exam/quiz taken during the semester. Computers can also be requested.

There will be a midterm exam and a final exam. Makeup exams will not be scheduled. Please plan for exams at these times and let us know about any exam conflicts during the first two weeks of the semester. If an emergency arises that conflicts with the exam times, email us as soon as possible. Emergency exam conflicts will be handled on a case-by-case basis.

Exam grading questions must be raised with the instructor within 72 hours after it is returned. If a regrade request is submitted for a part of a question on the exam, the grader reserves the right to regrade the entire exam and could potentially take points off.

Midterm : 10 March (Tentative)

Final : TBD

Office Hours

Instructors / TAs / peer mentors will hold office hours. See office hour page.

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• Introduction to Artificial Intelligence

Introduction to Artificial Intelligence (AI)

Delve into the exciting world of Artificial Intelligence under expert guidance with Introduction to Artificial Intelligence (AI) Certification by Coursera.

The highlights

Eligibility criteria, what you will learn.

• Admission Details

The syllabus

• Scholarship

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Course overview

Artificial Intelligence is an emerging field with numerous applications in different domains including science and technology, healthcare and even education. Students irrespective of their educational background are fascinated by the possibilities they can explore in AI. Thus, they need a course that not only confines them to theoretical aspects but also provides them practical exposure and leaves them with novice ideas for building a career.

In Introduction to Artificial Intelligence (AI) Certification by  Coursera , course participants, besides getting a basic insight into  Artificial Intelligence , will explore various cases and applications of AI. Introduction to Artificial Intelligence (AI) Certification Syllabus shall help them understand the terminology and concepts like  deep learning ,  machine learning and  neural networks . It will also provide exposure to out-of-the-book concepts in AI like ethics and bias.

Being a beginner-level course, learners need not possess any computer science or programming expertise beforehand. Anyone with or without a technical background shall acquire skills while studying at their own pace in online mode with flexible deadlines. The Introduction to Artificial Intelligence (AI) certification course is developed by  IBM  

• Seven-day full access free trial
• Flexible schedule
• No prior experience required
• Nine hours for completion
• Offered by IBM
• Shareable certificate
• Affordable programs and 7-day free trial
• Apply your skills with hands-on projects
• Course videos and readings
• Graded quizzes and assignments

Program offerings

• Course videos
• Graded quizzes
• Assignments

Course and certificate fees

Introduction to Artificial Intelligence (AI) Certification Fee is given below-

Introduction to Artificial Intelligence (AI) (audit only)	Free
Introduction to Artificial Intelligence (AI) - 1 month	Rs. 4,074
Introduction to Artificial Intelligence (AI) - 3 months	Rs. 8,149
Introduction to Artificial Intelligence (AI) - 6 months	Rs. 12,224

certificate availability

Certificate providing authority.

Work experience

Introduction to Artificial Intelligence (AI) Certification Online Course does not prescribe any work experience for learners in computer science or programming.

Certification qualifying details

In order to qualify for Introduction to Artificial Intelligence (AI) Certification, candidates firstly have to verify their name and identity and pass all assignments in this course. If this course is a cumulative graded course, they must achieve the course passing threshold. Lastly, they are required to pay the certification fee at the end of their free trial unless they are eligible for Coursera financial aid.

After completing the Introduction to Artificial Intelligence (AI) Certification syllabus

• Candidates will learn about terms  like deep learning, neural networks and machine learning
• They shall be able to mark the ethical considerations, bias and jobs in AI
• They shall discover applications and uses of AI and how it is transforming lives around the world
• They shall acquire the skills to reassure decision-makers about implementing an AI solution
• Learners will also study about the basic AI concepts and how it learns
• They will study the impact of the issues concerning AI on society
• They shall get a practical insight into demonstrating AI in action by utilising computer vision to classify images
• They will be trained enough to build an idea about future with AI
• They can get an informed awareness on the costs and benefits of AI implementation in different fields.
• Throughout the course, learners shall receive articulated advice from experts on starting a career in AI

Who it is for

Admission details.

The option of a free trial for seven days is provided to every participant who wishes to enrol for Introduction to Artificial Intelligence (AI) Certification Training. The procedure for the same and thereafter has been mentioned below-

Step 1: Visit the course page on the given link- https://www.coursera.org/learn/introduction-to-ai 

Step 2: Look for the option of “Enroll for Free Course”. 

Step 3: You may be able to pursue the course immediately after this in case you are logged in or Coursera. Else, you need to create an account first if you are a new user or log in if you already have an account.

Week 1: What is AI? Applications and Examples of AI

• Introducing AI
• What is AI?
• Optional: Tanmay’s journey and take on AI
• Generative AI Overview and Use Cases
• Impact and Examples of AI
• Optional: Application Domains for AI
• Some Applications of AI
• Optional: More Applications of AI
• Famous applications of AI from IBM
• Generative AI Applications
• Who should take this course?
• Lesson Summary
• Graded: What is AI? Applications and Examples of AI

Week 2: AI Concepts, Terminology, and Application Areas

• Cognitive Computing (Perception, Learning, Reasoning)
• Terminology and Related Concepts
• Machine Learning
• Machine Learning Techniques and Training
• Deep Learning
• Neural Networks
• Key Fields of Application in AI
• Natural Language Processing, Speech, Computer Vision
• Self Driving Cars
• Graded: AI Concepts, Terminology, and Application Areas
• Hands on Lab: Paint with AI (Optional)
• Hands-On Lab: Computer Vision

Week 3: AI- Issues, Concerns and Ethical Considerations

• Exploring Today's AI Concerns
• Exploring AI and Ethics
• Defining AI Ethics
• Understanding Bias and AI
• AI Ethics and Regulations
• AI Ethics, Governance, and ESG
• Foundations of Trustworthy AI: Operationalizing Trustworthy AI
• Precision Regulation for Artificial Intelligence
• Graded: AI Issues, Ethics and Bias

Discussion Prompt

• Reflection on AI issues, concerns, and ethics
• Hands-on Lab - Detect the Bias

Week 4: The Future with AI, and AI in Action

• The evolution and future of AI
• Future with AI
• The AI Ladder - The Journey for Adopting AI Successfully
• Advice for a career in AI
• Hotbeds of AI Innovation
• Tanmay’s Advice to Learn AI
• Polong’s Advice for a Job in AI
• Conclusion and Next Steps
• Course Team

Practice exercise

Scholarship details.

Enrollers who are willing to receive Introduction to Artificial Intelligence (AI) Certification but unable to afford the certification fee can opt for Financial aid by applying for the same through the course page. Next, they have to select the option of “Learn more and apply” visible near “Information about Financial Aid or Scholarships”. They must now submit a 150-word application. The resulting webpage would be the financial aid application form where learners have to do as directed. After affirming to pursue the course in totality and entering correct personal information, candidates have to click on “Continue” to complete their main form. They shall be required to enter their annual income, the minimum fee that they can afford, educational specifications, a short note on how they will utilise the learning outcomes, a statement of reasons for seeking Financial Aid and present employment information.

An intimation regarding the approval or rejection of the application shall be sent via mail where the learner can begin the course immediately for free or in audit mode in the former case. Each review may take around 15 days.

Note: The second process lays out that learners need to navigate through the course page and select the “Enroll for Free” option, after which they will be able to spot the option of “Financial Aid Available.” On selecting the same, they must follow a dialogue box and proceed with their application as stated above.

Acknowledging the immense capabilities of AI and the high probability of creating a high paying career in it, Introduction to Artificial Intelligence (AI) Certification Benefits learners by providing a detailed orientation of the existing and future jobs that have cropped and are cropping up in this field. Participants shall be advised by an IBM expert about how to set their career in AI that will only progress in the future. 

While working on a mini-project in this course, their practical knowledge and horizons will be amplified to the core. 15% of those who have pursued Introduction to Artificial Intelligence (AI) Certification Online Course have obtained a tangible career benefit while 13% got greater salaries or a promotion.

This course has been designed to cater to multiple specialisations or professional certificate programmes. This introductory course can be a great way to start with AI where newbies can precisely know everything they need to learn. The certificate post completion would be a great addition to one's LinkedIn profile or resumes.

Instructors

Mr Rav Ahuja Global Program Director IBM

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CSci 4511w, Spring 2020: Syllabus

Class information.

Time/Room:	Monday and Wednesday 4:00pm to 5:15pm in Bruininks Hall 220.
	(gini at umn.edu) office hours: Monday and Thursday 3:00-4:00 in Shepherd Lab 245 (612) 625-5582
TAs:	Robert Giaquinto

Date	Ch	Topics	Assignments	AIMA Slides
Week 1 -   Jan 22	1-2	Intro, intelligent agents
Week 2 -   Jan 27, 29	3	Problem solving and search	Writing 1 due (Wed Jan 29)
Week 3 - Feb 3, 5	3-4	Search and heuristic search	Homework 1 due (Wed Feb 5)
Week 4 - Feb 10, 12	4	Other search algorithms	Writing 2 due (Wed Feb 12)
Week 5 - Feb 17, 19	5	Game playing	Homework 2 due (Wed Feb 19)
Week 6 - Feb 24, 26
Week 7 - Mar 2, 4	6	Constraint satisfaction.	Writing 3 due (Wed Mar 4)
Week 8 - Mar 16, 18	7	Propositional logic	Homework 3 due (Wed Mar 18)
Week 9 - Mar 23, 25	8-9	First-order logic and resolution	Writing 4 due (Wed Mar 25)
Week 10 - Mar 30, Apr 1	9	Planning	Homework 4 due (Wed Apr 1)
Week 11 - Apr 6, 8	10	Planning
Week 12 - Apr 13, 15		Neural networks and deep learning	Writing 5 due (Wed Apr 15)
Week 13 - Apr 20, 22		Neural networks and deep learning	Homework 5 due (Wed Apr 22)
Week 14 - Apr 27, 29	20	Knowledge representation	Project report due (Wed Apr 29)

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Introduction to Artificial Intelligence

• Teaching with AI
• Introduction to AI and ChatGPT
• Creating AI Content
• Detecting AI Content

How can Educators respond to AI?

Chatgpt as an educational tool, analyzing ai writing tools.

• AI Organizations
• Citing Content Created with AI
• Articles and Commentaries
• Helpful Articles About AI

Considerations for using and addressing advanced automated tools in coursework and assignments  (University of Delaware)

AI-Generated Content in the Classroom: Considerations for Course Design  (University of Illinois)

ChatGPT and the Future of Writing Instruction (Youtube Video) 

Engaging with AI in your education and assessment  (Student briefings University College of London)

Teaching in the Age of AI  (Vanderbilt University Center for Teaching)  

Prompt engineering for educators – making generative AI work for you (University of Sydney)

Lang, J. (2023, April 4). How to Create Compelling Writing Assignments in a ChatGPT Age. The Chronicle of Higher Education.  https://www.chronicle.com/article/how-to-create-compelling-writing-assignments-in-a-chatgpt-age   (CHE account required)

Update the Syllabus

• Check out the Sentient Syllabus Project for potential language you might use in your syllabus about ChatGPT. http://sentientsyllabus.org .
• Update Your Course Syllabus for chatGPT . https://medium.com/@rwatkins_7167/updating-your-course-syllabus-for-chatgpt-965f4b57b003

Talk with students about academic integrity.

• Update your syllabus to include AI tools and discuss in class why academic integrity is essential to students.   http://sentientsyllabus.org
• Classroom Policies for AI Generative Tools document . (Lance Eaton) https://docs.google.com/document/d/1RMVwzjc1o0Mi8Blw_-JUTcXv02b2WRH86vw7mi16W3U/edit

Redesign assignments.

• Create assignments that cannot be completed by ChatGPT or other AI tools such as:  multimodal, higher-order thinking and learning activities; challenge-based learning; Shark Tank in the classroom; experiential learning; addressing wicked problems, and others.

Encourage risk-taking, productive struggle, and learning from failure.  

• Students can learn from failure as well as success and may be less likely to turn to tools like AI. ( Ofgang, 2021 ).
• The Role of Creative Risk Taking and Productive Failure in Education and Technology Futures  (Henriksen et al., 2021).

Be transparent about assignments.

• Consider how you might talk with students about the relationship between writing/research and learning. Explain why they need to write or do research, even when ChatGPT and Google can do that for them. 
• Use the  Transparency in Learning and Teaching framework.

Reconsider your approach to grading.

• “How to Ungrade” (Youtube video)

Shift from extrinsic to intrinsic motivation.

• Consider how to increase intrinsic motivation by giving students autonomy, independence, freedom, opportunities to learn through play, and/or activities that pique their interest based on their experiences and cultures. Read more about  motivational theories in education from Dr. Jackie Gerstein .

Utilize content not found in Chatbots

• Assign recent and obscure articles, readings and text that aren't, at this time, reflected in Chatbots and ChatGPT.  
• Assign recently produced videos. 

Field Trips

• Coordinate times to take your class to conduct field observations; students can note their observations and write a reflection about their experience. Virtual field trips are also an option.

Note: Before using ChatGPT please review their privacy and data policies.

Engage students in critiquing and improving ChatGPT responses.

• Computer science students might identify potential ways to revise ChatGPT generated code to reduce errors and improve output.
• Students might critically review the feedback ChatGPT provides on their writing and determine what is most helpful to their own learning. 
• Students could analyze, provide feedback on, and even grade text produced by ChatGPT as a way to prepare for peer review of their classmates’ work.
• Analyze how ChatGPT generates text for different audiences by asking ChatGPT to explain a concept for a 5 year old, college student, and expert. Analyze the difference in the way ChatGPT uses language.

Help students build their information literacy skills.

• Ask students to conduct an Internet search to see if they can find the original sources of text used to generate a ChatGPT response.
• Have students generate prompts for ChatGPT and compare and contrast the output. 
• Ask students to design their own tool to evaluate ChatGPT responses. 
• Ask ChatGPT to design a board game or invention related to the course content and then have students build a physical or digital model for the design/invention.

50 ChatGPT Prompts for Teachers

Questions About the AI Writing Tool

• Who created the AI writing tool?
• Who worked on training the AI writing tool?
• What dataset was used to train the AI writing tool? How does the diversity (or lack thereof) of the dataset influence the output of the AI writing tool? 
• Why was this tool created? 
• What are the objectives, aims, and values of the tool designer?
• What does the tool designer gain from your use of this tool? 
• How does the tool designer make money from the tool? 
• What do the privacy policies say? What are the terms of service/use and how much agency does the user have in their own participation and privacy? Are the privacy policy and terms of service easy to understand and read? Why do you think this is?
• What are the limitations of this tool? (e.g., ChatGPT is not connected to the Internet, and therefore, cannot draw connections to present-day events; ChatGPT has a limit for how much text you can upload)
• Who is the target audience for this tool? How do you know this? 
• Who is harmed and who benefits from this tool? (question borrowed from the  Civics of Technology curriculum )
• What are the unintended and unexpected benefits and consequences of using this tool? (question borrowed from the  Civics of Technology curriculum )

Questions About the Text Produced by the AI Writing Tool

• What information is presented in the text?
• What information is missing from the text? Why do you think that information is missing? (consider that ChatGPT generates text based on its training dataset)
• What type of language and word choices are used to convey ideas and information in the text?
• How are the language and word choices different from, or similar to, the way humans write? Why do you think that is?
• List three adjectives to describe your response to the text. Your adjectives can be based on your immediate emotional reaction or longer-term reflections. Why did you select those adjectives?
• Who is the target audience(s) for this text? How do you know this?
• How reliable, accurate, and credible is the text? How did you determine this? 
• What sources, if any, are cited? How accurate and relevant are those sources? 
• What biases are present in the text? Why might this be?
• What might be the original sources used to generate this text? Conduct an Internet search and see if you can find the original sources (it's likely more than one source!) that the AI tool used to generate this text. 
• After responding to the prompts in the "Questions About the AI Writing Tool," section above, how does this influence your thinking about the text generated by the AI writing tool? 

Source: https://edtechbooks.org/mediaandciviclearning/cmlguides#h2_xqej

Content used and modified with permission from Torrey Trust at University of Massachusetts Amherst. Retrieved January 19, 2023. https://docs.google.com/presentation/d/1Vo9w4ftPx-rizdWyaYoB-pQ3DzK1n325OgDgXsnt0X0/edit?usp=sharing

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• Last Updated: Jun 20, 2024 10:11 AM
• URL: https://guides.iona.edu/ai

UW-Madison CS 540 Fall 2020

Introduction to Artificial Intelligence

Course format announcement

Welcome to CS 540! This website is the home page for both section 001 and section 002 of CS 540 in the Fall 2020 semester. Because of the online format, the web site, Piazza discussion, and Canvas resources have been merged. Each section will still have its own office hours and grading. We’re excited to begin the semester with you.

Announcements

Course Information

Course learning outcomes : Students gain principles of knowledge-based search techniques; automatic deduction, knowledge representation using predicate logic, machine learning, probabilistic reasoning. Students develop applications in tasks such as problem solving, data mining, game playing, natural language understanding, and robotics.

Number of credits associated with the course : 3

How credit hours are met by the course : For each 50 minutes of classroom instruction, a minimum of two hours of out of class student work is expected. This course has two 75-minute classes each week over approximately 15 weeks, which amounts to the standard definition of a 3-credit course.

Prerequisite : (COMP SCI 300 or 367) and (MATH 211, 217, 221, or 275), mathematical maturity.

Optional Textbook : Artificial Intelligence: A Modern Approach (4th edition). Stuart Russell and Peter Norvig. Pearson, 2020. ISBN 978-0134610993.

Lecture Delivery

During lecture hours : Each lecture will be a series of short pre-recorded videos posted to Canvas before class. The lecture will be divided into three blocks. In each block, the instructor will broadcast a short video using BBCollaborate Ultra, pause for interactive Q&A, and deliver short quiz questions to clear up any confusion before proceeding to the next block. All video content will also be available to watch asynchronously outside of the lecture time. We suggest watching the content during class time, posting questions on Piazza during the lecture, and participating in the quiz. However, we acknowledge that your time constraints may not allow synchronous participation.

We will use Piazza for real-time Q&A during lectures. Please follow these rules:

To summarize: In class you watch videos with the instructor, ask / discuss questions on Piazza, and take short quizzes as poll .

Note: The distribution of CS540 final grades has been as follows. This is an approximation, and changes from semester to semester. The median student’s course grade is usually a low B or high BC. The percentiles refer to ranking based on the final weighted score.

Homework Policies

Homework assignments will include written problems and sometimes programming in Python. Frequently-asked questions (FAQs) on homework assignments will be e-mailed to the class mailing list. Homework is always due the minute before class starts on the due date. Late submissions will not be accepted. Assignment grading questions must be raised with the TAs within 72 hours after it is returned. Regrading request for a part of a homework question may trigger the grader to regrade the entire homework and could potentially take points off. Regrading will be done on the original submitted work, no changes allowed.

We will drop TWO lowest homework scores from your final homework average calculation. This drop is meant for emergency. We do not provide additional drops, late days, or homework extensions.

We encourage you to use a study group for doing your homework. Students are expected to help each other out, and if desired, form ad-hoc homework groups.

There will be a midterm exam and a final exam. All exams will be conducted online through Canvas. Students are allowed to choose their own exam schedule within a window of 24 hours. Makeup exams will not be scheduled. Please plan for exams at these times and let us know about any exam conflicts during the first two weeks of the semester. If an emergency arises that conflicts with the exam times, email us as soon as possible. Emergency exam conflicts will be handled on a case-by-case basis. Exam grading questions must be raised with the instructor within 72 hours after it is returned. If a regrade request is submitted for a part of a question on the exam, the grader reserves the right to regrade the entire exam and could potentially take points off.

Office Hours

Instructors / TAs / peer mentors will hold office hours in BBCollaborate Ultra, where the 250-student limit should not be an issue. In Canvas, go to the CS540 course, on the left menu you will find BBCollaborate Ultra. We set up an “Office hour test” session, you can join that session at any time to get familiar with the system. That session is not monitored, though, so don’t expect someone to chat with you – use the actual sessions that will be set up later. Students should attend the office hours for the section in which they originally enrolled.

Website template from Just the Class by Kevin Lin . Website content derived from Jerry Zhu and Hobbes LeGault’s CS 540 Spring 2020 site .

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Stanford Online

Artificial intelligence professional program.

Stanford School of Engineering

Per course $1,750 USD

10 weeks per course, 10-15 hours per week

Get Started

Artificial intelligence is transforming the world and helping organizations of all sizes grow, innovate, and make smarter decisions. The Artificial Intelligence Professional Program will equip you with knowledge of the principles, tools, techniques, and technologies driving this transformation. This online program provides rigorous coverage of the most important topics in modern artificial intelligence, including:

• Machine Learning
• Deep Learning
• Natural Language Processing and Understanding
• Supervised and Unsupervised Learning
• Reinforcement Learning
• Graph Neural Networks (GNNs)
• Multi-Task and Meta-Learning

The courses will equip you with the skills and confidence to:

• Build your own AI models and algorithms without the constraints of off-the-shelf solutions.
• Innovate and create new models, tools, and algorithms to tackle real-world challenges in AI.
• Effectively debug your code.
• Fine-tune and optimize model parameters for better results.
• Evaluate performance of AI models accurately.
• Implement generative language models.
• Perform few-shot and zero-shot learning with pre-trained language models.
• Understand research results and conduct your own research in the field.

Artificial Intelligence Professional Program FAQs

What you can expect

Course content is adapted from Stanford's on-campus graduate courses, allowing working professionals to explore AI topics at graduate-level depth, but with flexibility of schedule and scope. Courses are taught by Stanford faculty who are leading experts in their fields.	In each 10-week course, you will watch recorded lectures and work on coding and written assignments.
You will have a dedicated course facilitator available to address your questions through 1-on-1 calls, group office hours, Slack, or email. Course facilitators have completed the original graduate course and are working in industry.	You will join 100+ other learners eager to build their AI knowledge and skills. You will have opportunities to create study groups and learn more about the field from each other.
Each course has its own Slack community where learners can ask questions, get feedback, and exchange ideas.	Join live sessions such as group assignment office hours, timely topic discussions, and informal coffee chats.

What you need to get started

Prior to enrolling in your first course, you must complete a short application (15-20 minutes). The application allows you to share more about your interest in joining, as well as verify that you meet the prerequisite requirements needed to make the most of the experience:

• Proficiency in Python: Coding assignments will be in Python. Some assignments will require familiarity with basic Linux command line workflows.
• College Calculus and Linear Algebra: You should be comfortable taking (multivariable) derivatives and understand matrix/vector notation and operations.
• Probability Theory: You should be familiar with basic probability distributions (Continuous, Gaussian, Bernoulli, etc.) and be able to define concepts for both continuous and discrete random variables: Expectation, independence, probability distribution functions, and cumulative distribution functions.

NOT SURE IF THIS PROGRAM IS RIGHT FOR YOU?

Learn about the differences between the graduate and professional AI Programs .

Flexible Enrollment Options

Individual enrollments.

$1,750 per course

Courses are online and the pace is set by the instructor. You will be part of a group of learners going through the course together. You will have scheduled assignments to apply what you've learned and will receive direct feedback from course facilitators.

Groups and Teams

Special Pricing

Have a group of five or more? Enroll as a group and learn together! By participating together, your group will develop a shared knowledge, language, and mindset to tackle challenges ahead. We can advise you on the best options to meet your organization’s training and development goals.

What You'll Earn

You’ll earn a Stanford Professional Certificate in Artificial Intelligence when you successfully complete either:

• Three courses in the AI professional education program, OR
• Two courses in the AI professional education program AND one course in the AI Graduate Program.

Your blockchain-verified digital certificate will allow you to showcase your achievements on LinkedIn and other platforms, validate credentials with employers, and highlight your expertise.

Academic Director

Christopher Manning

Thomas M. Siebel Professor of Machine Learning

Computer Science

Christopher Manning is a Professor of Computer Science and Linguistics at Stanford University and Director of the Stanford Artificial Intelligence Laboratory. He works on software that can intelligently process, understand, and generate human language material. He is a leader in applying Deep Learning to Natural Language Processing, including exploring Tree Recursive Neural Networks, neural network dependency parsing, the GloVe model of word vectors, neural machine translation, question answering, and deep language understanding. He also focuses on computational linguistic approaches to parsing, robust textual inference and multilingual language processing, including being a principal developer of Stanford Dependencies and Universal Dependencies. Manning is an ACM Fellow, a AAAI Fellow, an ACL Fellow, and a Past President of ACL. He has coauthored leading textbooks on statistical natural language processing and information retrieval. He is a member of the Stanford NLP group (@stanfordnlp) and manages development of the Stanford CoreNLP software.

Teaching Team

Emma Brunskill

Associate Professor

Emma Brunskill is an Assistant Professor at Stanford University. Her goal is to increase human potential through advancing interactive machine learning. Revolutions in storage and computation have made it easy to capture and react to sequences of decisions made and their outcomes. Simultaneously, due to the rise of chronic health conditions, and demand for educated workers, there is an urgent need for more scalable solutions to assist people to reach their full potential. Interactive machine learning systems could be a key part of the solution. To enable this, her lab's work spans from advancing theoretical understanding of reinforcement learning, to developing new self-optimizing tutoring systems that they test with learners and in the classroom. Their applications focus on education since education can radically transform the opportunities available to an individual.

Stefano Ermon

Stefano Ermon is an Assistant Professor in the Department of Computer Science at Stanford University, where he is affiliated with the Artificial Intelligence Laboratory and a fellow of the Woods Institute for the Environment. His research is centered on techniques for scalable and accurate inference in graphical models, statistical modeling of data, large-scale combinatorial optimization, and robust decision making under uncertainty, and is motivated by a range of applications, in particular ones in the emerging field of computational sustainability.

Chelsea Finn

Assistant Professor

Chelsea Finn is an Assistant Professor in the Computer Science Department at Stanford University. Her lab, IRIS, studies intelligence through robotic interaction at scale, and is affiliated with Stanford Artificial Intelligence Lab (SAIL) and the Statistical ML Group. She is interested in how algorithms can enable machines to acquire more general notions of intelligence through learning and interaction, allowing them to autonomously learn a variety of complex sensorimotor skills in real-world settings.

Jure Leskovec

Jure Leskovec is an Associate Professor of Computer Science at Stanford University. Leskovec's research focuses on the analyzing and modeling of large social and information networks as the study of phenomena across the social, technological, and natural worlds. He focuses on statistical modeling of network structure, network evolution, and spread of information, influence and viruses over networks. Problems he investigates are motivated by large scale data, the Web and other on-line media. He also does work on text mining and applications of machine learning.

Percy Liang

Associate Professor Computer Science

Percy Liang is an Assistant Professor in the Computer Science department. He works on methods that infer representations of meaning from sentences given limited supervision. What's particularly exciting to him is the interface between rich semantic representations (e.g., programs or logical forms) for capturing deep linguistic phenomena, and probabilistic modeling for allowing these representations to be learned from data. More generally, he is interested in modeling both natural and programming languages, and exploring the semantic and pragmatic connections between the two. 

Tengyu Ma is an Assistant Professor of Computer Science and Statistics at Stanford University. His research interests broadly include topics in machine learning and algorithms, such as non-convex optimization, deep learning and its theory, reinforcement learning, representation learning, distributed optimization, convex relaxation (e.g. sum of squares hierarchy), and high-dimensional statistics. He received my Ph.D. from the Computer Science Department at Princeton University where he was advised by Professor Sanjeev Arora. As an undergrad, Tengyu Ma studied at Andrew Chi-Chih Yao's CS pilot class at Tsinghua University.

Christopher Potts

Linguistics

Christopher Potts is a Professor of Linguistics and, by courtesy, of Computer Science and the Director of The Center for the Study of Language and Information (CSLI). In his research, he uses computational methods to explore how emotion is expressed in language and how linguistic production and interpretation are influenced by the context of utterance. He is the author of the 2005 book The Logic of Conventional Implicatures as well as numerous scholarly papers in computational and theoretical linguistics.

Christopher Ré

Christopher (Chris) Ré is an Associate Professor in the Department of Computer Science at Stanford University in the InfoLab who is affiliated with the Statistical Machine Learning Group, Pervasive Parallelism Lab, and Stanford AI Lab. His work's goal is to enable users and developers to build applications that more deeply understand and exploit data. His contributions span database theory, database systems, and machine learning, and his work has won best paper at a premier venue in each area, respectively, at PODS 2012, SIGMOD 2014, and ICML 2016. In addition, work from his group has been incorporated into major scientific and humanitarian efforts, including the IceCube neutrino detector, PaleoDeepDive and MEMEX in the fight against human trafficking, and into commercial products from major web and enterprise companies. He cofounded a company, based on his research, that was acquired by Apple in 2017. He received a SIGMOD Dissertation Award in 2010, an NSF CAREER Award in 2011, an Alfred P. Sloan Fellowship in 2013, a Moore Data Driven Investigator Award in 2014, the VLDB early Career Award in 2015, the MacArthur Foundation Fellowship in 2015, and an Okawa Research Grant in 2016.

Dorsa Sadigh

Dorsa Sadigh is an Assistant Professor in the Computer Science Department and Electrical Engineering Department at Stanford University. Her work is focused on the design of algorithms for autonomous systems that safely and reliably interact with people.

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1/8	Introduction.	Chapter 1. Introduction slides: , .
1/13, 1/15, 1/20, 1/22, 1/27	Search.	Chapters 3, 4. which uses the files , , , , and . Search slides: , . More search slides: , If you would like to learn more about linear and integer programming, you can go to ; especially the and lecture notes might be useful.
1/29, 2/3	Game playing.	Chapter 6. Slides: , . , .
2/5-2/17	Logic.	Chapters , 8, 9. Slides: Propositional logic: , . First-order logic: , .
2/19,2/24	Planning.	Planning slides: , . Chapter .
2-26 - 4/2	Probabilistic reasoning.	Chapters 13-17. Probability slides: , . Bayes nets slides: , . Markov processes and HMMs slides: , .
4/2 - 4/16	Decision theory. Markov decision processes, POMDPs. Game theory.	Chapters 18, 19, . Decision theory slides: , . MDP/POMDP slides: , . Game theory slides: , .
4/21	Machine learning.	Chapters 18, 19, , 21. (You do not need to know this in great detail since we spent so little time on this in class, the chapters are just in case you're interested.) Machine learning slides: , .
4/21	Wrapping up.	Wrapping up slides: , .

8/26	Introduction.	Chapter 1. Introduction slides: , .
8/28 - 9/16	Search. Constraint satisfaction and optimization problems.	Chapters 3, 4, . Search slides: , . More search slides: , . For more about linear and integer programming, you can go to ; especially the and lecture notes might be useful. Helper files: , .
9/16, 9/18	Game playing.	Chapter 6. Slides: , .
9/23-9/30	Logic.	Chapters , 8, 9. Propositional logic slides: , . First-order logic slides: , .
10/2, 10/7	Planning.	Planning slides: , . Chapter .
10/16-11/11	Probabilistic reasoning.	Chapters 13-17. Probability slides: , . Bayes nets slides: , . Markov processes and HMMs slides: , .
11/13, 11/18, 11/20	Decision theory. Markov decision processes, POMDPs. Game theory.	Chapters 18, 19, . Decision theory slides: , . MDP/POMDP slides: , . Game theory slides: , .
11/25	Machine learning.	Chapters 18, 19, , 21. (You do not need to know this in great detail since we spent so little time on this in class, the chapters are just in case you're interested.) Machine learning slides: , .
11/25	Wrapping up.	Wrapping up slides: , .

Fall 1398-1399

Assignments

You can download the assignments here (in PDF format). Also check out assignment’s pages for any additional info.

• Assignment #1 - Search problems  
• Assignment #2 - A star and CSP  
• Assignment #3 - Minimax  
• Assignment #4 - MDP & RL  

Integrating AI into assignments

Main navigation.

Here we offer strategies and perspectives on integrating AI tools into assignments and activities used to assess student learning.

Creating your course policy on AI

• An effective syllabus works to motivate learning, define goals, explain course structure, and provide support to students as they learn.
• Be clearly stated and specific
• Clarify the context or conditions of allowable AI use
• Explain processes and consequences for non-compliance
• Have a thoughtful pedagogic rationale in support of student learning
• Connect to support resources
• Show support for student well-being

Outcomes for this module

In this module, we will analyze activities and assignments used for assessing learning, provide student-centered perspectives, and offer strategies for developing assessment activities and assignments that integrate student use of generative AI chatbots.

After completing this module, you should be able to:

• Describe why your assessment activities are meaningful to learners.
• Identify and clarify the learning objectives of your assessment activities.
• Identify relevant strategies that can be applied to assessment activities in your course.
• Empathize with student perspectives on using AI in course assessment activities.

Warm-up with a metacognitive exercise

As you begin to explore, think about what you already know and the opinions you may already hold about the educational aspects of AI chatbots. This metacognitive exercise can help you identify what you want to explore and what you already understand. Making connections to what you already know can deepen your learning and support your engagement with these modules.

Begin with the prompt, “Describe an assignment or assessment activity that integrated technology in a way that was effective and engaging for your learning,” and respond to the poll below.

Unpacking your assessment activities and assignments

When designing or adapting an activity or assignment used to assess learning, whether you integrate AI or not, we encourage you to consider two questions: why is this meaningful, and what are students supposed to learn from it?

Define why it is meaningful

Students can learn better when they are motivated and can make meaningful connections to coursework (Headden & McKay, 2015). We might assume that students’ motivations focus on their grades, but that assumption does not provide the full picture, and when applied in isolation it is not likely to sustain deep learning. Articulating what makes an activity meaningful, motivational, and memorable for students can help you create an engaging activity or assignment that enhances student learning and motivation.

Concerning AI chatbots, perhaps the activity or assignment addresses AI in ways that prepare students for future careers, enhance their social connections, or touch upon broader issues they care about. We encourage you to talk with your students about what they find meaningful to inform the design of your activities and assignments. What leads them to want to engage?

Also, reflect on why the assignment is meaningful to you. Is it simply convenient to implement (and standard in your experience as a student and teacher) or does it connect to something deeper in your pedagogy? Perhaps the assignment reinforces the norms and values that you share with other professionals in your discipline, allows you to connect with students in more meaningful ways, builds foundational skills for other parts of the curricula, or explores emergent opportunities and challenges with AI for your field.

Define what students are intended to learn

Next, identify and clarify the underlying learning objectives of the assignment or activity. The objective should describe the observable skills or behaviors students will have learned to perform after completing the activity. Clearly articulated learning objectives can help you develop activities that support learning and assessments that accurately measure student learning.

When thinking about AI chatbots and how they impact writing, you might ask yourself, “What are the underlying learning objectives being addressed through writing?” Instructors may assign writing tasks to assess how students engage with content. In the past, teachers could assume with good reason that a student producing coherent writing must have engaged with the content to generate writing that makes sense. However, we might also question this assumption about the automatic connection between coherent writing and deep engagement. The advent of generative AI has certainly exacerbated this.

Do you ask your students to write to demonstrate and reinforce content knowledge? Do they write to analyze and critique a position? Do they write to formulate arguments and cite evidence? Do they write as a form of creative expression? When you think about the available options, you can likely develop many ways for students to learn and demonstrate these skills with or without writing. Ultimately, honing in on the underlying learning objectives can help you integrate generative AI tools into an assignment.

Students can benefit from understanding how AI works and the educational opportunities and challenges that it presents. Consider offering the content in the modules in this guide to your students as supplemental reading or as part of a class activity.

Strategies for implementing AI into activities and assignments

As you think through how you might address or integrate AI tools in an assessment activity or assignment, we encourage you to consider a range of possibilities related to the specific aims of your course and the needs of your students. Here we offer a variety of pedagogical strategies for you to consider. We present these strategies in the context of students using AI chatbots, but they also apply to contexts without AI. Remember why your assignment is meaningful in relation to your learning objectives to help you select appropriate strategies.

Leverage multiple modalities

Consider ways to diversify when and where you assess student learning and the formats students use to express what they’ve learned.

Use more in-class assignments

Strategies like the flipped classroom model assign lecture content as homework and use the in-class time for learning activities (Lage et al., 2000). You can use this in-class time to integrate more low-stakes assessment activities during which you can better guide students toward using AI in ways that support learning.

Multiple modes of expression

Students may differ in how they can best articulate what they know. Using multiple modalities of expression, such as having students complete assignments that require speaking or graphic representations instead of only written text, stands out as an established strategy within the Universal Design for Learning framework that could apply here. While chatbots primarily generate written text, other AI tools can generate music, graphics, and video. You can thus create assessment activities that integrate multiple modalities at once.

For example, if you are assessing students’ understanding of cultural exchange in the ancient world, students might create a mind map or timeline to visually represent important trends, events, or concepts covered in the assigned readings. AI might then be used to generate images of artifacts, portraits, or cityscapes based on historical descriptions.

Make grading practices clear

Consider ways to clarify for students how they are being graded and what is expected of them.

Require robust citation

Have students learn about and adopt more robust citation practices, especially if they use AI tools for writing. You might begin with conversations about what plagiarism entails and why ethics matter in higher education and your discipline. Then connect students to resources on citation and documentation .

If you and your students decide to use AI tools, you can find style guidelines about citing AI-generated text for APA style and MLA style . These guidelines advise writers to cite the AI tool whenever they paraphrase, quote, or incorporate AI-generated content, acknowledge how they used the tool (for brainstorming, editing, and so on), and vet secondary sources generated by AI. For example, students could include citations for AI in the Works Cited section of their work and also include a statement describing why and how they used AI chatbots.

Establish and communicate clear assessment criteria

Try to bring assessment activities, learning objectives, and evaluation criteria into alignment. For example, if your objectives and assessments center around students proposing a solution to an open-ended problem, then the evaluation criteria might touch upon the feasibility, impact, or comprehensiveness of the proposed solutions. The criteria can vary a lot depending on your content and course, but your students benefit when you communicate these criteria and the purpose and reasoning behind them (Allen & Tanner, 2006).

For example, when integrating AI chatbots into a writing task for students, you might put more weight on the quality of their ideas and the validity of cited sources and less weight on structure, grammar, and word choice. You might then create a rubric that you discuss with students in advance so they have a clear understanding of what will guide you in assessing their work.

Assess learning throughout the course

Consider ways to assess student learning throughout your course as opposed to assessing mostly at the end of the course.

Emphasize the process

You may be able to more effectively assess student learning during the different stages of the process as opposed to assessing learning based on their finished work (Xu, Shen, Islam, et al., 2023). Whether or not students use AI tools, they can benefit from segmenting a large project into smaller components with multiple opportunities for feedback and revision. Also, consider how you might adjust grading criteria or grade weights to put more emphasis on the process.

For some steps in the thinking process, such as brainstorming ideas, formulating a position, and outlining a solution, allowing students to use AI tools might benefit their process. For example, you might have students begin with low-stakes free-writing, such as brainstorming, then use AI chatbots to explore possible areas for further investigation based on the ideas students generate through their exploratory writing. Students might then critique and revise the AI-generated ideas into an outline.

Leverage formative feedback

Teachers provide formative feedback to students throughout the learning process to stimulate growth and improvement. Formative feedback can help students identify misunderstandings, reinforce desirable practices, and sustain motivation (Wylie et al., 2012). You and the teaching team might provide feedback directly to students or you might facilitate students giving feedback to each other. You might then assess how students follow up on feedback they receive.

You can use AI tools to inform your feedback to students or generate feedback directly for students. AI tools could provide instant, individualized feedback efficiently and frequently, supplementing the feedback provided by your teaching team. For example, you might share your existing assignment, rubric, and sample feedback with the chatbot and give it instructions on when and how to give feedback. Importantly, you should review feedback generated by chatbots for accuracy and relevance. Refine and save the prompts that work best. You might later share the prompts you’ve developed with students so they may use them to generate feedback themselves.

Make assignments more meaningful

Consider how you might make your assignments more relatable and meaningful to your students.

Personalize assessments

When done thoughtfully, connecting assessments to the personal experiences, identities, and concerns of students and their communities can help to motivate and deepen learning (France, 2022). You might also connect assignments to contexts specific to Stanford, your course, or your specific group of students.

With AI, you or your students might generate practice questions on topics that came up during a specific class discussion or generate analogies for complex concepts based on their interests and backgrounds. You might ground an assessment activity in local contexts, such as having your engineering students propose a plan to improve Lake Lagunita.

Use real-world assessment tasks

Assignments that leverage real-world problems, stakeholders, and communities that students are likely to engage with in their work lives can be motivational and valid ways of evaluating a student’s skills and knowledge (Sambell et al., 2019).

For example, students might work with real (or AI-simulated) business or community partners to develop a prototype product or policy brief. Students might have more time to work with those stakeholders and refine their proposal concepts if they can use AI tools to assist with time-consuming tasks, such as summarizing interview transcripts, writing a project pitch statement, or generating concept images.

AI itself might provide a relevant topic of study for your course. For example, you might examine AI as part of a discussion in a course about copyright and intellectual property law. Or you might analyze AI companies such OpenAI or Anthropic as case studies in a business course.

Assess more advanced learning

Consider ways you might assess more advanced or wider-ranging learning goals and objectives.

Emphasize metacognitive reflection

Metacognitive reflection activities, where students think about what and how they learn, can help students improve their learning (Velzen, 2017). You might use polls, discussion activities, or short writing exercises through which students identify what they already know about the topic, what they learned, what questions remain, and what learning strategies they might use for studying.

AI chatbots can help guide the reflection process like this reflection tool being developed by Leticia Britos Cavagnaro at Stanford d.school . Or perhaps students complete some activities with AI, then reflect on how it benefits or hinders their learning, and what strategies they might use to best leverage AI for learning.

Prioritize higher-order thinking

While students should develop mastery over foundational skills such as understanding concepts, identifying key characteristics, and recalling important information, practicing higher-order thinking skills, such as solving complex problems, creating original works, or planning a project, can deepen learning. For example, you might frame student essays as a defense of their views rather than a simple presentation of content knowledge. You might adjust assessment criteria to prioritize creativity or applying skills to new contexts.

Prioritizing higher-order thinking can encourage students to use AI tools to go beyond simply generating answers to engaging deeply with AI chatbots to generate sophisticated responses. Students could conduct preliminary research to find reliable sources that verify or refute the claims made by the AI chatbots. AI chatbots might then generate feedback, provide prompts for further reflection, or simulate new contexts.

Putting it all together

Here we offer a practical example: first, a typical assignment as usually designed, and then how you could enhance the assignment with some strategies that integrate AI chatbots.

When thinking about your course, start with small changes to one assignment and steadily expand upon them. Try to use AI chatbots for your other work tasks to build your fluency. Talk with students and colleagues about how the changes to your course work out concerning student engagement and learning. When integrating AI into an existing assignment, begin with an assignment that already has clearly defined learning objectives and rationale. Begin by using AI or other technology to supplement existing parts of the process of completing the assignment.

More examples of AI assignments

• AI Pedagogy Project from metaLAB (at) Harvard
• Exploring AI Pedagogy from the MLA-CCCC Joint Task Force on Writing and AI
• TextGenEd: Continuing Experiments, January 2024 Collection from WAC Clearinghouse

Example of an assignment without AI

Currently, your students in an epidemiology course write essays summarizing the key concepts of an academic article about the socio-determinants of diabetes . This assessment activity has meaning because it focuses on a foundational concept students need to understand for later public health and epidemiology courses. The learning objective asks students to describe why socio-economic status is a strong predictor for certain diseases. Students write a five-page essay about a disease that can be predicted by socio-economic status including at least three additional citations. Students complete the essay, which counts for 30% of the final grade, before the final exam.

An example of an assignment that integrates AI

Using some of the strategies in the above sections, you might redesign this assignment to integrate the use of AI chatbots. Keep in mind that you would likely make small changes to a major assignment over multiple quarters. Consider some of the ideas below.

A meaningful assignment

The redesigned assessment activity carries more meaning to students because they might have personal experience of some communities adversely affected by these kinds of diseases, and public health issues like this intersect with other social injustices that students have expressed concern about.

Learning objectives

The objectives of the assessment activity include that students will be able to:

• Describe how this disease affects particular communities or demographics
• Explain the difference between correlation and causality regarding socioeconomic status and the disease
• Propose a public health intervention that could help to address this issue

Assignment elements with AI

Students generate explanations of medical terminology in the selected articles to aid with reading comprehension. They generate several analogies for the core concept that apply to their own life experiences and communities. Students share these analogies in a Canvas forum graded for participation. Instructors provide general feedback in class.

Informed by the article, students then prompt a chatbot with biographical stories for two fictional characters from communities they care about incorporating differing socio-economic factors. Then they guide the chatbot in generating a dialogue or short story that illustrates how the two characters could have different health outcomes that might correlate with their socio-economic status. Students might use AI image generators for illustrations to accompany their stories. Students submit the work via Canvas for evaluation; the teacher shares exemplars in class.

Using an AI chatbot prompt provided by the instructor, students explore possible ideas for public health interventions. The provided prompt instructs the chatbot only to help students develop their ideas rather than suggesting solutions to them. With the aid of the chatbot, the students develop a public health intervention proposal.

Assignment elements without AI

Students discuss the differences between correlation and causation, critically analyze the generated characters and stories, and address any biases and stereotypes that surfaced during the activity. You facilitate the discussion with prompts and guidelines you developed with the aid of AI chatbots. Students write an in-class metacognitive reflection that you provide feedback on and grade for completion.

Students draw posters that summarize their proposed intervention. They critique and defend their proposals in a classroom poster session. Students complete a peer evaluation form for classmates. You evaluate the posters and their defenses with a grading rubric that you developed with the aid of an AI chatbot.

Students write an in-class reflection on their projects summarizing what they have learned over the length of the project, how the activities aided their learning, and so on. This is submitted to Canvas for grading and evaluation.

Student-centered perspective on using AI for learning

When thinking about integrating generative AI into a course assignment for students, we should consider some underlying attitudes that we, the authors, hold as educators, informed by our understanding of educational research on how people learn best. They also align with our values of inclusion, compassion, and student-centered teaching. When thinking through ways to integrate AI into a student assignment, keep the following perspectives in mind.

AI is new to students too

Like many of us, students likely have a wide range of responses to AI. Students may feel excited about how AI can enhance their learning and look for opportunities to engage with it in their classes. They may have questions about course policies related to AI use, concerns about how AI impacts their discipline or career goals, and so on. You can play a valuable role in modeling thoughtful use of AI tools and helping students navigate the complex landscape of AI.

Work with students, not against them

You and your students can work together to navigate these opportunities and challenges. Solicit their perspectives and thoughts about AI. Empower students to have agency over their learning and to think about AI and other technologies they use. Teaching and learning are interconnected and work best in partnership. Approach changes to your teaching and course to empower all students as literate, responsible, independent, and thoughtful technology users.

Look at AI and students in a positive light

Education as a discipline has repeatedly integrated new technologies that may have seemed disruptive at first. Educators and students typically grapple with new technology as they determine how to best leverage its advantages and mitigate its disadvantages. We encourage you to maintain a positive view of student intentions and the potential of AI tools to enhance learning. As we collectively discover and develop effective practices, we encourage you to maintain a positive and hopeful outlook. We should try to avoid assuming that most students would use generative AI in dishonest ways or as a shortcut to doing course assignments just because some students might behave this way.

Assess and reinforce your learning

We offer this activity for you to self-assess and reflect on what you learned in this module.

Stanford affiliates

• Go to the Stanford-only version of this activity
• Use your Stanford-provided Google account to respond.
• You have the option of receiving an email summary of your responses.
• After submitting your responses, you will have the option to view the anonymized responses of other Stanford community members by clicking Show previous responses .

Non-Stanford users

• Complete the activity embedded below.
• Your responses will only be seen by the creators of these modules.
• Course and Assignment (Re-)Design , University of Michigan, Information and Technology Services
• ChatGPT Assignments to Use in Your Classroom Today , University of Central Florida

Works Cited

Allen, D., and Tanner, K. (2006). Rubrics: Tools for Making Learning Goals and Evaluation Criteria Explicit for Both Teachers and Learners. CBE - Life Sciences Education. 5(3): 197-203.

Ashford-Rowe, K., Herrington, J., & Brown, C. (2014). Establishing the critical elements that determine authentic assessment. Assessment & Evaluation in Higher Education, 39. https://doi.org/10.1080/02602938.2013.819566&nbsp ;

Bijlsma-Rutte, A., Rutters, F., Elders, P. J. M., Bot, S. D. M., & Nijpels, G. (2018). Socio-economic status and HbA1c in type 2 diabetes: A systematic review and meta-analysis. Diabetes/Metabolism Research and Reviews, 34(6), e3008. https://doi.org/10.1002/dmrr.3008&nbsp ;

CAST. (n.d.). UDL: The UDL Guidelines. Retrieved January 22, 2024, from https://udlguidelines.cast.org/&nbsp ;

Exploring AI Pedagogy. (n.d.). A Community Collection of Teaching Reflections. Retrieved January 22, 2024, from https://exploringaipedagogy.hcommons.org/&nbsp ;

France, P. E. (2022). Reclaiming Personalized Learning: A Pedagogy for Restoring Equity and Humanity in Our Classrooms (2nd ed.). Corwin.

Headden, S., & McKay, S. (2015). Motivation Matters: How New Research Can Help Teachers Boost Student Engagement. Carnegie Foundation for the Advancement of Teaching. https://eric.ed.gov/?id=ED582567&nbsp ;

Hume Center for Writing and Speaking. (n.d.). Documentation and Citation. Retrieved January 22, 2024, from https://hume.stanford.edu/resources/student-resources/writing-resources… ;

Lage, M. J., Platt, G. J., & Treglia, M. T. (2000). Inverting the Classroom: A gateway to creating an inclusive learning environment. Journal of Economic Education, 31(1), 30-43.

metaLAB (at) Harvard. (n.d.). The AI Pedagogy Project. Retrieved January 22, 2024, from https://aipedagogy.org/&nbsp ;

MLA Style Center. (2023, March 17). How do I cite generative AI in MLA style? https://style.mla.org/citing-generative-ai/&nbsp ;

Office of Community Standards. (n.d.). What Is Plagiarism? Retrieved January 22, 2024, from https://communitystandards.stanford.edu/policies-guidance/bja-guidance-… ;

Sambell, K., Brown, S., & Race, P. (2019). Assessment to Support Student Learning: Eight Challenges for 21st Century Practice. All Ireland Journal of Higher Education, 11(2), Article 2. https://ojs.aishe.org/index.php/aishe-j/article/view/414&nbsp ;

The WAC Clearinghouse. (n.d.). January 2024. Retrieved January 22, 2024, from https://wac.colostate.edu/repository/collections/continuing-experiments… ;

U-M Generative AI. (n.d.). Course and Assignment (Re-)Design. Retrieved January 22, 2024, from https://genai.umich.edu/guidance/faculty/redesigning-assessments&nbsp ;

Van Velzen, J. (2017). Metacognitive Knowledge: Development, Application, and Improvement. Information Age Publishing. https://content.infoagepub.com/files/fm/p599a21e816eb6/9781641130240_FM… . ISBN 9781641130226. 

Wylie, E. C., Gullickson, A. R., Cummings, K. E., Egelson, P., Noakes, L. A., Norman, K. M., Veeder, S. A., ... Popham, W. J. (2012). Improving Formative Assessment Practice to Empower Student Learning. Corwin Press.

Xu, X., Shen, W., Islam, A. A., et al. (2023). A whole learning process-oriented formative assessment framework to cultivate complex skills. Humanities and Social Sciences Communications, 10, 653. https://doi.org/10.1057/s41599-023-02200-0  

Yee, K., Whittington, K., Doggette, E., & Uttich, L. (2023). ChatGPT Assignments to Use in Your Classroom Today. UCF Created OER Works, (8). Retrieved from https://stars.library.ucf.edu/oer/8  

You've completed all the modules

We hope that you found these modules useful and engaging, and are better able to address AI chatbots in your teaching practice. Please continue to engage by joining or starting dialogues about AI within your communities. You might also take advantage of our peers across campus who are developing resources on this topic.

• Institute for Human-Centered Artificial Intelligence
• Accelerator for Learning
• Office of Innovation and Technology , Graduate School of Education

We are continuing to develop more resources and learning experiences for the Teaching Commons on this and other topics. We'd love to get your feedback and are looking for collaborators. We invite you to join the Teaching Commons team .

Learning together with others can deepen the learning experience. We encourage you to organize your colleagues to complete these modules together or facilitate a workshop using our Do-it-yourself Workshop Kits on AI in education. Consider how you might adapt, remix, or enhance these resources for your needs. 

If you have any questions, contact us at [email protected] . This guide is licensed under  Creative Commons BY-NC-SA 4.0 (attribution, non-commercial, share-alike) and should be attributed to Stanford Teaching Commons.

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Join this free online course to learn about the importance and principles of ethics in the context of artificial intelligence (AI). Discover how SAP implements AI ethics in the development, deployment, use, and sale of AI systems, and explore the concept of generative AI in a newly added course unit.

Course information

Course summary.

SAP believes that artificial intelligence (AI) has the potential to unlock all kinds of opportunities for businesses, governments, and society. However, AI also has the potential to create economic, political, and social challenges. The speed at which the technology has moved into common usage has outpaced the necessary guidance from government policymakers regarding the sustainable and safe development of AI. For these reasons, the development, deployment, use, and sale of AI systems at SAP needs to be governed by a clear, ethical set of rules.

In this course, you’ll learn the importance and principles of AI ethics, how AI ethics is implemented at SAP, and which rules SAP employees need to follow during the development, deployment, use, and sale of AI systems. Furthermore, you’ll explore the concept of generative AI and the specific challenges in its ethical development, deployment, and use.

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• Starting from: March 12, 2024, 09:00 UTC. ( What does this mean? )
• Duration: The course is open for 6 weeks
• Effort: 2-4 hours in total
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• Course closure: June 5, 2024, 9:00 UTC
• Course language: English
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Course Content

Unit 1: Introduction to AI at SAP Unit 2: AI ethics at SAP Unit 3: The 3 pillars of SAP’s AI ethics policy Unit 4: Operationalization of the AI ethics policy Unit 5: Assessing the risk of AI use cases Unit 6: The ethics of generative AI

Target Audience

• Artificial intelligence (AI) stakeholders who develop, deploy, use, sell, and interact with AI or are affected by it, both inside and outside of SAP
• Anyone interested in the topics of AI and machine learning as well as AI ethics and responsible AI

Course Requirements

A basic understanding of AI and machine learning is an advantage.

Further Learning

• Creating Trustworthy and Ethical Artificial Intelligence
• Generative AI at SAP

About Further Content Experts

Saskia welsch.

Saskia Welsch is a working student at AI Workbench. She supports SAP’s trustworthy AI workstream, which is responsible for operationalizing SAP’s AI ethics guiding principles and SAP’s global AI ethics policy.

Saskia is studying for a Master’s degree in Science and Technology Studies at TU Munich, where she is researching the reciprocal relationship between technology and society.

Previous Version of This Course

Previous version of this course is available here: AI Ethics at SAP (Update Q4/2023) (November 21 through December 20, 2023)

Course contents

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This course was rated with 4.5 stars in average from 2198 votes .

Certificate Requirements

• Gain a Record of Achievement by earning at least 50% of the maximum number of points from all graded assignments.
• Gain a Confirmation of Participation by completing at least 50% of the course material.

Find out more in the certificate guidelines .

This course is offered by

Dr. sebastian wieczorek.

Dr. Sebastian Wieczorek is global lead of the AI ethics initiative at SAP, which provides company-wide guidelines on how to apply AI in a human-centric way.

Sebastian is also vice president of AI technology at SAP, and responsible for development teams in Europe and Asia that build AI platform services across all SAP offerings.