Situated Imaginaries: Designing AI Futures with Computer Science Teaching Assistants
A solid CHI honorable-mention contribution: the paper is strongest as a situated, mixed-methods synthesis of TA AI imaginaries, not as a universal theory of education AI. Its value lies in reframing TAs as knowledge workers and in showing how institutional context shapes both use and speculation.
Axes Lens
Rare contribution shape, typical evidence profile. The point here is not a score. It is to show what kind of claim the paper makes, and whether the evidence pattern is unusual or baseline in this 268 -review set.
Contribution shape
- Knowledge form
- descriptive knowledge typical · 92/268
- Novelty type
- synthesis typical · 16/268
- Abstraction level
- practice typical · 85/268
- Generalization target
- user population typical · 75/268
- Validation mode
- mixed methods typical · 136/268
Evidence profile
- Evidence strength
- strong typical · 158/268
- Claim alignment
- strong typical · 231/268
- Overclaim risk
- medium typical · 210/268
Review Summary
This paper’s contribution is best understood as a context-rich CHI synthesis about how a specific educational labor group imagines and uses AI, rather than as a general-purpose AI-in-education framework. The authors’ central move is to reposition computer science teaching assistants as AI-supported knowledge workers embedded in instructional infrastructure. That is a meaningful departure from common-sense automation narratives, because it foregrounds the ongoing human labor, negotiation, and institutional mediation that make educational AI possible. The novelty is not a new interface or algorithm; it is the cross-institutional typology and the interpretive account of situated TA uses of AI, built from design workshops, surveys, and artifacts. The evidence base is reasonably strong for that kind of claim: 131 TAs across two U.S. universities is a substantial sample for a qualitative/mixed-methods CHI paper, and the paper’s own framing aligns with the evidence it reports. At the same time, the authors are appropriately cautious about scope. Their workshops emphasized near-term, pragmatic visions, they acknowledge that critical reflection could have been more prominent, and they explicitly note limits from the two-institution setting and AI-referenced recruitment. So the paper should not be read as establishing broad causal effects or universal TA attitudes. Instead, it offers a well-supported descriptive and normative contribution: a grounded account of how disciplinary norms, institutional structures, and intermediary labor shape AI imaginaries in computing education. That makes it a strong honorable-mention paper with clear relevance to HCI’s interest in situated design, human–AI collaboration, and the politics of educational labor.
What Changed
Canon before
Prior CHI work on AI in education and computing education has often centered students, instructors, or automation-oriented tool support; this paper instead centers teaching assistants as situated intermediaries whose labor, norms, and institutional context shape AI imaginaries.
Departure from common sense
The paper’s framing is counterintuitive in a useful way: it treats TAs not as a narrow support role to be automated, but as knowledge workers whose ongoing human labor and negotiation are part of the instructional infrastructure. That shifts the unit of analysis from tool replacement to socio-technical maintenance and makes the human dimension of AI use analytically central.
Actual novelty
The paper’s main novelty is a cross-institutional typology of situated TA uses of AI, derived from design workshops with CS TAs at two U.S. universities. Rather than offering a generic list of AI uses, it organizes how TAs imagine and use AI within their specific pedagogical and institutional conditions, surfacing opportunities, tensions, and ethical dilemmas.
Evidence
The paper reports design workshops with 131 computing TAs across two U.S. universities and analyzes surveys plus design artifacts to produce a cross-institutional typology and thematic findings about situated AI use, institutional shaping, and ethical dilemmas. The evidence supports a contextual, qualitative contribution rather than a broad causal claim.
“ Drawing on surveys and design artifacts, we (1) develop a cross-institutional typology of situated TA uses of AI, revealing opportunities and tensions; (2) show how TAs’ visions of AI are shaped by disciplinary norms, institutional structures, and their intermediary position as student-instructors; and (3) reveal ethical dilemmas”
actual novelty · Abstract + Findings 6.1 (typology of twelve themes) · confidence 0.80
“ By framing TAs as knowledge workers, our work calls attention to how AI tools in education depend on ongoing human labor and negotiation and how TAs form part of the often-invisible infrastructure that sustains university instruction, which cannot be fully replaced by AI”
departure from common sense · Discussion 7.1 Teaching Assistants as Knowledge Workers in the Age of AI · confidence 0.72
“ Our findings contribute to HCI by positioning TAs as AI-supported knowledge workers in the education domain; illustrating how design and speculation are shaped by people’s situated understandings of AI and their institutional contexts; and identifying a core tension in which TAs simultaneously preserve and erode the human dimensions of their work, with implications for future instructional tools and human–AI collaboration”
limitation · Limitations and Future Work 8 · confidence 0.88
“ We report on a series of design workshops with 131 computing (CS) TAs across two U”
validation scope · Abstract + Workshop Design/Evaluation + Findings 6.1-6.3 · confidence 0.82
Limits
Method limits
The workshop method emphasizes near-term, pragmatic visions and relies on self-reported surveys and design artifacts rather than observed classroom deployment or longitudinal outcomes. The authors also note that critical reflection elements of speculative methods were not foregrounded enough.
Deployment limits
Findings are grounded in two U.S. universities and in participants recruited through materials that explicitly referenced AI, so transfer to other institutions, countries, or TA populations may be limited. The results are best read as context-sensitive patterns rather than universal TA behavior.
Boundary conditions
The contribution is strongest for computing TAs in U.S. higher education settings where AI is already salient and where TAs occupy an intermediary student-instructor role. It is less directly generalizable to non-computing disciplines, non-university settings, or contexts with different labor structures and AI norms.
Position in field
This sits at the intersection of HCI, AI in education, and computing education as a situated, participatory account of how a specific educational labor group imagines AI futures. Its field value is in reframing TAs as AI-supported knowledge workers and in showing how institutional context shapes speculative design outcomes.