AI as Unreliable Narrators in (CS) Education
Benjamin Xie (he/they)
Embedded Ethics Postdoctoral Fellow
Stanford University
Overview
Activity (3 min): �On the AI Iceberg�benji.phd/kf
I’m a CSEd + HCI Researcher
Computing Education (CSEd)
HCI research
Previously: MIT, UW, Code.org�Currently: Stanford Human-Centered AI Institute (HAI), Ethics Center
I design critical & equitable human-data interactions
Critical: connect data to social structures & history
Equitable: More equal distributions of inputs & outputs
Human-data interactions: learning across boundaries
P(reported non-binary) gets item correct)
P(reported female) gets item correct
benjixie.com/las21
P(reported non-binary) gets item correct)
P(reported male) gets item correct
Assimilating to {unfamiliar, dominant} norms
AI & Tyranny of the Majority
For talk, AI refers to data-driven Gen AI tools
Many kinds of AI tools (“reasoners”): such as predictors, classifiers, recommenders, planners, generators
Focus of this talk on Generative AI tools built upon Large Language Models (e.g. ChatGPT for text generation, DALLE-2 for image generation)
AI is built by select few to maximize profit
AI tools tend to perpetuate dominant social norms.
Sasha Luccioni
Which images are more “racy”?
Example: “Racy” Images & Shadowbanning
AI tools rate photos of women as more sexually suggestive
Impacts:
Equity (in CS Edu)
Def’n: equal & adequate distribution of inputs (resources, opportunities) or outputs (growth, outcomes) �Levinson, Gergon, & Brighouse 2022
Equity in CS Edu:
▲resources
◼️opportunities
⬟growth
⬢outcomes
more challenging
ECEP Alliance
EdTech & Inequities
"I believe that the motion picture is destined to revolutionize our educational system" -Thomas Edison, 1922
Same tools used differently by different schools.
(Ed)Tech tends to perpetuate and/or exacerbate existing social inequities
AI in K-12 education
Many framings of AI
Model-Centric AI: improve model performance while treating data as static, objective
Data-Centric AI: improve model & data iteratively (Jarrahi, Memarian, & Guha 2023)
Human-Centered AI: social and ethical implications of AI on individuals, communities, societies
Ex: Learning facial recognition…
Model-Centric AI: Different techniques
Data-Centric AI: Imbalances in data result in performance differences by gender, skin tone
Human-Centered AI: Why Black communities do not want facial recognition in policing
Joy Buolamwini / Gender Shades. 2018
Pew Research 2021
India Times, 2017
“Dual Exploration” w/ AI & Disciplinary Learning
Co-Design w/ Cross-Disciplinary Teachers
Study w/ 8 U.S. high school STEM & humanities teachers teachers. Most from Title I schools which served Hispanic and Latinx, Black and African-American students.
Goal: Understand teachers’ perspectives on AI, opportunities & limitations in using Gen AI tools in class
Perceived Dichotomy: Technical vs Ethical
“you can sort of think about [AI] is a universal system....In my engineering class, we do a whole unit on universal systems... So I guess it's in context of what you're trying to bring [AI] into. So for [English Language Arts]... the learning objectives are a little different. I like this lesson, because... they're learning the technical things... And less about, like, `how to make an argument' or `what ethics are.'”
- Engineering teacher serving Latinx students
Opportunities: AI as augmentative, empowering
Empowering: Enable students with less art skills to create and share art
Augmentative: ChatGPT to convey students’ ideas in more professional tone
21
“How can this algorithm can assist [students] in writing a more professional business plan?”
- Econ teacher serving Black/African- American students
“could have like a competition or something, and hang their pieces in the hallway. So everyone can say, `hey? I created this,' because not everyone's an artist. But I look at [DALL·E] as a tool to help them create art.”
- CS (web development, augmented reality) teacher serving white students
Engaging with Limitations of AI
Limitations to reflect on affordances
Alchemy of prompt engineering as hindrance?
22
�“the discussion I want to have is is [AI generated art] even close? And then to hit ‘ways in which AI algorithms would need to improve in order to …become stronger…’ [For example,] hands have got to be better. Anatomy's got to be better. This is not going to be a good resource until X, Y, and Z.”�- animation & digital media teacher
“I think I would like [students] to get started with… drawing it themselves first, so they don't get bogged down with trying to find the perfect wording and then get really frustrated and be like oh, `I can't create what I want to create, because I don't have the words for it,'” �- art teacher serving Hispanic students
Co-designed, Cross-disciplinary, Critical AIEd
Bridging technical and ethical/social perspectives of AI
“Dual exploration:” AI learning to support disciplinary learning (and vice-versa)
Designing to make limitations of AI tools part of learning experiences
AI, CSEd, and Equity
Many Types of Programmers
CS courses often target professional programmers
Many program as part but not all of careers (end-user programmers, Nardi 1993)
Many more want to be engage in computing discourse (conversational programmers, Chilana, Singh, & Guo 2016)
Ethical & Socials Lens to CSEd
Pluralizing CS by teaching about computing through technical, social, and ethical lenses
At Stanford, I embed ethics into CS courses
ethics content
students’ prior knowledge,
beliefs, expectations
prior CS knowledge
My approach to embedding ethics in CS:
GenAI tools changing what to teach
Tools can generate syntactically correct code instantly
=> Less emphasis on code writing?
Greater emphasis on reading, comprehending, debugging, verifying correctness?
GenAI tools risk over-reliance
Learners have greater risk of over-reliance on AI tools �(Perry et al. 2022, Jalil et al. 2023, Wang et al. 2023)
Existing tools do not convey uncertainty, afford customizability, reflect dominant social norms
=> must also teach self-regulation to align AI usage with goals
CS expertise
risk
Pluralizing CSEd & AI
Many different programmers, different skills to teach, ways to incorporate AI
Mitigate risks of over-reliance on AI tools by teaching right skills (self-regulation, debugging)
conversational
end-user
professional
types of programmers
Model-Centric AI: improve model performance while treating data as static, objective
Data-Centric AI: improve model & data iteratively
Human-Centered AI: social and ethical implications of AI on individuals, communities, societies
different framings of AI
Closing Thoughts
Learning isn’t individual!
Learning process is one in which learners can realize their human dignity and potential (to participate in existing systems, challenge oppressions) without enduring a process of dehumanization.
Learning computing provides learners with an opportunity to engage with powerful tools that they can use to unseat individual and collective social oppressions.
AI as Unreliable Narrators in (CS) Education
Takeaways:
Ongoing and future work
Recommended Reading:
conversational
end-user
professional
types of programmers
Model-Centric AI: improve model performance while treating data as static, objective
Data-Centric AI: improve model & data iteratively
Human-Centered AI: social and ethical implications of AI on individuals, communities, societies
different framings of AI
Benjamin Xie (he/they) Stanford University