About

Table of contents

  1. Course Rationale
  2. Course Description
  3. Expected Learning Outcomes
  4. Assignments
  5. Course Expectations
    1. Required
    2. Optional
  6. Grading
  7. Evaluation Criteria
  8. Class Policies
    1. Late Submissions
    2. Academic Dishonesty
    3. Use of AI
    4. Technolog Requirements
    5. Technical Assistance

Course Rationale

Artificial Intelligence (AI) is increasingly being integrated into many walks of life, and AI learning is deemed a 21st century skill with applications in several professional careers, including education. AI algorithms have also found applications in learning environments, for personalizing learning content, scaffolding, and learning analytics, and it is imperative for professionals in learning ecosystems to gain conceptual and practical AI knowledge, as well as participate in creating AI tools. To prepare students for a future workforce, and enable them to be responsible creators of emerging technology, this course engages students in gaining basic AI competency, discuss key topics in AI in relevance to applications in learning, and reflect on the ethical implications of integrating AI in our learning environments, while simultaneously building AI tools to enhance learning. This course goes over essential literature in AI for learning, and societal and ethical implications of AI, and engages students in designing human-centered AI solutions to scaffold learning.

Course Description

This course will be going over the fundamentals of designing AI systems, the nuts and bolts of machine learning algorithms, designing learning technology with AI, and discussing the societal and ethical implications of AI on learning environments. Students will explore, study and critique current work in using AI systems in learning environments. Students will also engage in seminal work around AI for learning, and societal and ethical implications of AI. They will gain practical experience in using AI tools, and creating their own. They will engage in group work around designing AI-powered systems for scaffolding learning, while reflecting on the long-term implications of the system. This project-based course is meant to be an introduction to Artificial Intelligence systems and tools, particularly for learning applications. Key topics include: artificial intelligence, machine learning, neural network architectures, human-AI interaction, creative ML, large language models, and AI in learning. Students are expected to complete this course with (1) a functional AI-powered tool, or an implementable framework for using AI in learning, and (2) a paper describing the design of your project. There are no prerequisites to this course, but students will be asked to use certain Python libraries, hence, basic familiarity with, or eagerness to learn about programming, is preferred.

This course has five key elements: (1) Fundamentals of AI, (2) Designing Human-centered AI (3) AI in learning environments, (4) Prototyping learning tools with AI, and (5) Societal and ethical implications of AI systems, that are intertwined across 14 class sessions.

Expected Learning Outcomes

Students should be able to:

  • Identify key processes in the working of machine learning systems
  • Identify key processes in the working of large language models
  • Examine learner needs, and identify opportunities to utilize AI in learning environments.
  • Fine-tuning ML algorithms for specific use cases
  • Apply design principles from explainable AI, fair AI, creative AI, and transparent AI systems.
  • Critically evaluate the impact of digital technologies on the opportunities for professional achievement and social equity
  • Demonstrate critical thinking about the design and social, cultural and ethical impact of technology through written and verbal exercises and class debate.
  • Adapt an ML system to a chosen learning application

Assignments

Each week will consist of an assigned reading reflections and assignment (unless specified otherwise). You may find the readings, assignments, and due dates in the course curriculum. Reading reflections will be a form available on the calendar tab of this website. All assignments can be submitted on the group Google Drive. All reading reflections and assignments will be due 24 hours prior to the class (5:00 PM on Mondays).

Course Expectations

Required

  • Weekly in-person weekly class meeting
  • Weekly reading assignments will be available online (Due 24 hours prior to the class meeting: Monday at 5:00 PM EST)
  • Weekly mini-assignment (Due 24 hours prior to the class meeting: Monday at 5:00 PM EST)
  • Weekly lecture assignments available online to be completed before class meetings.
  • Final project paper
  • Final project source materials or code repository

Optional

  • Weekly office hours
  • Supplemental reading

Grading

  • Mini-assignments & project updates (40%)
  • Final project paper and materials (30%)
  • Class participation including online discussion and class presentations (20%)
  • Final project presentation (10%)

Evaluation Criteria

A = Excellent This work demonstrates comprehensive and solid understanding of course material and presents thoughtful interpretations, well-focused and original insights and well-reasoned analysis. “A’ work includes skillful use of source materials and illuminating examples and illustrations. “A” work is fluent, thorough and shows some creative flair.

B = Good This work demonstrates a complete and accurate understanding of course material, presenting a reasonable degree of insight and broad level of analysis. Work reflects competence, but stays at a general or predictable level of understanding. Source material, along with examples and illustrations, are used appropriately. “B” work is reasonable, clear, appropriate and complete.

C = Adequate/Fair This work demonstrates a basic understanding of course material but remains incomplete, superficial or expresses some important errors or weaknesses. Source material may be used inadequately or somewhat inappropriately. The work may lack concrete, specific examples and illustrations and may be hard to follow or vague.

D = Unsatisfactory This work demonstrates a serious lack of understanding and fails to demonstrate the most rudimentary elements of the course assignment. Sources may be used inappropriately or not at all. The work may be inarticulate or extremely difficult to read.

Grade percentage equivalents: A 93-100, A- 90-92, B+ 87-89, B 83-86, B- 80-82, C+ 77-79, C 73-76, C- 70-72, D+ 67-69, D 63-66, D- 60-62, F Below 60

Class Policies

Late Submissions

Late work will result in a reduction of 15% of that assignment per day late. Reviewing work off-schedule as well as tracking down missing work significantly detracts from time I could spend on class planning, providing feedback on student work, and meeting with students.

You are granted late extensions in cases of illness, family obligations, religious observance, or other events that fall under the University guidelines for an excused absence. If you have a good reason for needing more time, please let me know as soon as possible BEFORE the due date. Please communicate your time extension needs as soon as you are able to. I will do my best to support you in being able to get the most out of this course, and try to provide accommodations whenever possible.

Academic Dishonesty

“Academic integrity is the guiding principle for all that you do…. You violate the principle when you: cheat on an exam; submit the same work for two different courses without prior permission from your professors; receive help on a take-home that calls for independent work; or plagiarize. Plagiarism, whether intended or not, is academic fraud. You plagiarize when, without proper attribution, you do any of the following: copy verbatim from a book, article, or other media; download documents from the Internet; purchase documents; paraphrase or restate someone else’s facts, analysis, and/or conclusions; copy directly from a classmate or allow a classmate to copy form you.” (See School of Education Bulletin, 2006/8, p. 172) or steinhardt.nyu.edu/policies/academic_integrity.

Use of AI

This is an interesting one for this course, isn’t it? You will of course be using AI in different parts of this course. You may also choose to collaborate with AI to complete your various to-do’s. However, I trust you to think deeply about using AI in ways that benefit you as a learner the most. For instance, asking AI to do your reading reflections will lead to little learning and novel insights for you, and may lead to no interesting contributions from you in classroom discussions. However, brainstorming ideas, going back and forth with an AI to flesh out your ideas is welcome. Prioritize your agency and intellectual contribution in your use of AI tools. I would highly recommend not using AI in a one-shot manner to complete your assignments. That is a lose-lose situation, because you learn little, and on the other end, I can typically tell, and I learn little about your unique individuality. Finally, extra points for discliamers in your assignments where you declare interesting ways in which you chose to use AI (or chose NOT to use AI).

Technolog Requirements

  • Computer access (we recommend against using a mobile device). Users can access public Mac and PC computers throughout the Libraries. NYU Students, faculty, and staff can also borrow wifi-enabled laptops. Visiting this link to learn more. Please let me know if access to minimum course technology is a concern and I will help you find a solution.
  • Python 3.0 or above installed on your computer
  • AI APIs (coming soon)

Technical Assistance

If you need technical assistance at any time during the course or to report a problem with NYU Brightspace or NYU Zoom you can: