Ethical, Critical, and Professional Use of Generative AI in Teaching and Learning

Professor: Dr. Samuel McCready & Dr. Jen Jenson
Semester: Summer 2026
Course information: https://met.ubc.ca/courses/etec-565T/
Course Website: https://blogs.ubc.ca/ethicalai/

Course Description

Intelligence has always been an artificial construct, and no more so than when being used to refer to what is now broadly defined and organized as “artificial intelligence” (AI). First coined by John McCarthy in the 1950s, AI refers to a machine or algorithm that simulates human intelligence. Machines can, sort of, mimic human intelligence, but not without a whole lot of human input (think meta and training data). In fact, so much human input is used in these “artificially intelligent” systems that Kate Crawford convincingly argues such systems are “neither artificial nor intelligent,” and comments extensively on wider social structures and systems of power that are in flux as AI encroaches upon and disrupts existing political, economic, social, and educational frameworks (Crawford, 2021).

In 2026, few people in industrialized nations will not have heard of generative AI (Gen AI) and its uses for creating content (Chat GPT, Claude, Co-pilot, Gemini, images [Dalle-E], and music [AVIA], for example). In this course, we will explore Gen AI from the form it is in as of June/July 2026: its application/s, uses, biases, and blind spots. Taking an exploratory approach, we will examine how GenAI is used in our everyday realities, whether at home, school, or work, with a view to understanding the affordances and limitations of GenAI for learning and teaching. GenAI is already coming to dominate information and media spaces – our task is to understand what this means now and in the near future.

Positive visions of AI futures for education (or automation, or institutional efficiency) that are often uncritical are at this point well-rehearsed and offer little in the way of a point of entry aimed at better understanding what GenAI does, what it costs, and why it matters. In this course, we will take a critical view, examining the sustainability of AI, including the costs to power a single search, the resources it takes to maintain AI, as well as the human costs of “training” GenAI. Readings will include general and politically nuanced accounts of “AI”, as well as specific readings on its use/s in education, medicine, and business. Students will be invited to explore potential applications, challenges and ethical considerations of using Gen AI in educational contexts, as well as the wider technological, ethical, social, and political implications of Gen AI use.

Please join the course slack here: https://join.slack.com/t/etec565t66csu-q4e9847/shared_invite/zt-424ruy4qc-_MjKuBCN8MuZHcdGOIyWBg

Course Objectives and Learning Outcomes

Trace the Evolution of AI Research Paradigms: Students will analyze the major shifts in AI research methodologies and philosophical approaches, identifying key influences and turning points, including deconstruction of the “AI hype cycle” by critically examining the history of AI, differentiating between actual advancements and overblown promises, identifying key turning points and influential figures.

Build an understanding of Algorithms: Students will explore the fundamental concepts of algorithms as the sets of rules and instructions that power AI systems. They will investigate how these algorithms process data, make decisions, and produce outputs, understanding their crucial role in shaping the results of AI applications in decision making in real world settings such as health care, finance, and criminal justice). Students will learn to identify the types of algorithms used and the potential consequences of that use.

Evaluate the Epistemological Implications of AI: Students will investigate how AI impacts knowledge creation, dissemination, and validation, considering its effects on trust, expertise, and evidence-based decision-making, especially in relation to education.

Consider the Environmental and Resource Footprint of AI: Students will read about the environmental impact of AI technologies throughout their life cycle (manufacturing, operation, disposal), and resource consumption. They may choose to consider such implications in Assignment 1.

Design Strategies for Mitigating Algorithmic Bias: Students will develop and evaluate methods for identifying, preventing, and mitigating biases within AI algorithms and systems, considering both technical solutions and broader societal interventions.

Explore Pedagogical Implications of Integrating AI in Education: Students will go beyond merely using tools to fundamentally redesign learning experiences informed by AI’s potential and limitations.

Consider Pedagogical Applications of AI, Beyond Content Generation: Students will examine innovative applications of AI in educational settings that go beyond content creation, such as AI-assisted design thinking for curriculum development or AI-driven tools for personalized learning experiences, to understand their potential advantages and challenges in enhancing teaching and learning.

Readings & Viewings

**Please Note** While almost all of the readings listed here can be found in UBC library, a few of these readings may require you to employ a little google help.

Asynchronous readings:

Week 1 (July 2nd-10th): A starting place for GenAI: Introductions, Ideas, and Experimentation.

Readings:

Coleman, B. (2021). Technology of The Surround. Catalyst: Feminism, Theory, Technoscience, 7(2), 1–21.
Crawford, K. (2021). Atlas of AI: Power, Politics and the Planetary Costs of Artificial Intelligence. Yale University Press.
Hao, K. (2025). Empire of AI: Dreams and nightmares in Sam Altman’s OpenAI. Penguin Press. Chapters 2, 12, 18.

Viewing (Optional):

Roher, D., & Tyrell, C. (Directors). (2026). The AI Doc: Or How I Became an Apocaloptimist [Film]. Playgrounds; Cottage M; Fishbowl Films.

Veatch, V. (Director). (2026). Ghost in the Machine [Film]. Distributed Artificial Intelligence Research Institute (DAIR); PBS Documentaries; Kinema.

Assignments

Assignment 1 (online, Week 1): Investigate biases and blind spots in AI systems (15%); Due: Friday, July 10th by 11:59pm PDT. Submitted via Slack.

Description: Interact with two or three AI-based tools (e.g., image generators, text generation tools, etc.), giving those tools the same prompt or set of prompts to generate material. Analyze the output to identify potential biases and blind spots, such as gender or racial bias, or important perspectives overlooked/missing. Offer a reflection on how these biases and blind spots might manifest in real-world applications of the selected AI tools and highlight some strategies for identifying and addressing them by pulling from your own user experiences as well as course materials and readings. Submissions will consist of an archive (or screen capture) of the prompts and their outputs in the selected GenAI tools, as well as the reflection/best practices document. For this second part, multimodal submissions are welcome (e.g., ~1000 words of text, or a 5–7-minute video presentation, a 10-15 minute podcast, a multimodal Canva or Genially presentation, etc.).

Assignment 2 (online, Week 2): Evaluation Heuristic and Testing (15%); Due: Friday, July 17th by 11:59pm PDT. Submitted via Slack.

Description: Create an evaluation framework (heuristic) for using GenAI in a professional setting by first creating a use for GenAI in that setting and then creating a framework for evaluating its use. In producing an evaluation framework, reflect upon material and scholarship from the course that relates to AI ethics and sustainability and integrate some of these concepts into your work. What principles involved in the ethical use of AI/GenAI that we have encountered in this course are important to adhere to in your use case? How can we assess the potential environmental impacts of integrating GenAI use in this case? What questions need to be asked about GenAI to assess its utility and efficacy in your chosen setting? Once you have created this evaluation framework, test it by using GenAI in the manner you have identified and determine whether your framework works as an effective guide. Submission for this assignment will consist of the developed heuristic and a short reflection (~500 words) on its test use. We think it is useful to think about this assignment as following a few basic steps:

Think about something in your workplace or a workplace (a task, a “to do” etc.) that a genAI might also be able to do or to assist with. we’ve given a few possible examples but this can include things like: having genAI assist you with writing assessment feedback for students; having genAI assist you with planning a learning unit or module; having genAI auto transcribe and annotate your meetings. This could also be something that you already use a genAI for, it doesn’t have to be something new. If you are currently using Gemini, or Claude, or Co-pilot, or GPT (or something else) in this way, feel free to focus on that.
You (not AI) devise a framework for evaluating how well or successfully your genAI tool is at assisting or completing the task you have outlined in step 1. In devising this framework, you may consider looking at current guidelines for AI use to help you think about what might represent ‘best practices’. The web blog includes a link to the UBC guidelines. Also, trust your understanding of your workplace and expectations – you know what good work looks like, use that to help you frame what effective genAI assistance looks like.
Run a test case, where you have your selected genAI perform the task you have outlined in step 1. Depending on the size of the task you are looking to have genAI assist with (or do fully), you can just run a condensed test. You want enough information here that you can think/reflect upon in order to discuss how well you think the genAI has done measured against your evaluation framework (step 2). So, if you’re looking at using AI to help with lesson planning, have it help you plan a hypothetical lesson; if you’re using it to transcribe and annotate meetings, have it listen to a recorded or performed meeting.
Report your findings. Begin this by outlining what you tasked the genAI with doing, and with describing the evaluation framework you developed for measuring its success — In addition, you can consider questions such as the following: How well did the genAI do? How well DO YOU think your evaluation framework did as a means for assessing the genAI? Is there anything the tool did particularly well, or less well? Has this experiment revealed for you any of the strengths or limitations of the genAI tool? What are your takeaways from this work? What do we need to be thinking about as it regards the ongoing integration of genAI into daily work (and life)?

Your submission here consists of two things: the developed evaluation framework and your write-up (step 4). If you prefer, you can also submit this as a podcast (that YOU record, no AI voiceover please) or short video. In addition, feel free to incorporate any visuals or other multimodalities that you wish. Start here: https://genai.ubc.ca/guidance/teaching-learning-guidelines/

Assignment 3 (In Person): 15%: A critical evaluation of journalism of AI/GenAI

During the in-person Institute, and working with your group, you will select several media pieces/articles from a curated list of recent news on, about, and related to AI/genAI. Your task will be to read through your selection together and write up a brief precis (~200 words) for each one. This critical evaluation goes beyond a summary of the article/piece and seeks to situate its ideas, events, themes, concepts, and arguments within a broader matrix of discourse related to AI/genAI. An easy way to start it to think about the topics with which a given piece/article intersects in its discussion of AI: Is the article raising points related to history/the past, economics, politics, culture, media, education, or sustainability (or multiple of these)?

You should seek to direct the readers towards what they believe the author(s) central argument is and engage with that argument critically. In other words, these precis should include a brief description of what the reading is about, what it argues, and then proceed to engage with that argument. Do you find that the author(s) make a compelling case for their perspective? What kinds of evidence do they rely upon? Is there something you feel is missing in their work? Are there questions they have not considered that you feel are worth exploring? Are there perspectives that you feel are missing in their work?

Assignment 4 (In Person and online, July 20th-27th): Teaching and Learning with AI ‘Stories’ (55%).

Description: The major assignment for this course, broken into several parts, will involve working in groups and using GenAI to devise a learning module that is centered around a specific subject and learner cohort, and that is accompanied by the submission of a ‘testimonial’ production that provides insights and information regarding the ethical and productive integration of GenAI in education. You will decide which subject and target user you wish to develop this module for, and which GenAI tool(s) you will use to assist in design and development. As your group navigates this activity, describe your use of GenAI tools with reference to accuracy and reliability, potential biases, ethical and pedagogical implications. The final submission will consist of a ‘proof of concept’ for the designed learning module, and a produced ‘testimonial’ document that outlines the design and development process, working with GenAI tools, steps undertaken to integrate AI thoughtfully and ethically, relevant connections to core concepts and topics from the course, and perspectives regarding best practices for GenAI use in education based upon your experiences.

Part 1: Selection and critical evaluation of GenAI tools (5%), due at the end of our in-person session on day one, July 20th (4:00pm). Submitted via Slack.

Description: For the first part of this assignment, produce a brief mockup (1-2 pages) that outlines the specific GenAI tools you are thinking about or intend to use for your project and explains potential uses and limitations of each tool. Reflect upon the unique ethical and applicative questions that are raised by each specific GenAI you have identifed, paying close attention both to what these tools can offer, and the critical questions they may raise about data security, learning design, user (student) participation, assessment, curricular goals, and sociocultural/algorithmic bias.

Part 2: Proposal (10%), due by 8pm PST on day two of our in-person sessions, July 21st. Submitted via Slack.

Description: After completing the first stage of the assignment, move on to assembling a formal proposal for your work (~1200 words). There is no one-size-fits-all template for this proposal, though there are some major points that you will be asked to speak about in your work, which are noted below. You can use whatever format/presentation style works best for your group, and multimodality is welcome for this task. In addition to these listed points, you may include anything else that you view as important to know about your project.

A name or title for your proposed project, which could be related to the topic that will be centered in your learning module.
A breakdown of group members and member roles as they are understood at this point.
The subject matter and learner cohort you have chosen for your ‘proof of concept’ learning module, along with a very brief (i.e. short paragraph) description of what has led you to make these choices in terms of content.
What platform your group will be using to develop this module (i.e. Google Sites, Genially, Canva, Prezi, Figma, Fectar, among many others). We are design agnostic, so you should feel empowered to explore and decide upon a platform that you believe will fit well with the scope of your project.
What GenAI tools your group intends to use to assist in the design and development process. In addition, in as much detail as you can offer, how you intend to integrate them into your work. What opportunities and challenges do the integration of GenAI raise for your project? Take some time here to expand upon the work you did in part 1 RE: the ethical and applicative questions you introduced.
Any information you can offer regarding design and use goals for your learning module. What are you intending for or hoping for users to do with this? What are you hoping they learn via interacting with it? Are you aligning your learning module with specific curricular goals? If so, which ones?
Initial thoughts regarding the format for your testimonial. Are you thinking about doing a purely textual write-up? A short video? A multimodal presentation?
Any other information you wish to share about your work.

Part 3: Presentation (15%) July 24th, in class

Description: On the final day of our in-person sessions, we will be reserving time for each group to present their work. In your presentation, take the audience through your project, including a “live” demonstration of your proof-of-concept learning module where you walk us through its subject matter and highlight some of its features while speaking to the ways in which you have integrated GenAI into the design and development process. Also go over other relevant experiences of the work, including the design and building process, encountering and overcoming challenges, and key takeaways. Some things to consider as you are putting your presentations together are: have your views on AI/GenAI and its use in teaching and learning shifted as you have worked through this assignment and engaged with the other work on the course? How have you tried to ensure that your use of GenAI in your project is thoughtful, careful, and considerate of concerns related to bias, blind spots, and ethics?

Part 4: Designed Object (25%) DUE at 11:59pm PDT, July 27th; submit via Slack

Use GenAI to devise a learning module that is centered around a specific subject and learner cohort. You will decide which subject and target user you wish to develop this module for, and which GenAI tool(s) you will use to assist in design and development. Develop a proof-of-concept learning module following the plan you lay out in your proposal. The idea here is not a polished, fully functioning learning module, but rather a good faith effort at trial and error and experimentation with the topic(s), platform(s), and GenAI tools. We want to acknowledge here that we are not expecting expertise or precision; this project is intended to be productive, thoughtful, and fun collaborative work that gets us thinking about and working with GenAI in a hands-on fashion. More specific details for what a proof-of-concept can look like for this project will be outlined during our Asynchronous and In-person sessions. Groups will submit their proof-of-concept Design Object via Slack on Monday, July 27th, by 11:59pm PDT.

ERICA Squared by Erica Holdaway

ETEC 565T

Ethical, Critical, and Professional Use of Generative AI in Teaching and Learning

Course Description

Readings & Viewings

Assignments