Students exercise ethical agency in GenAI use through human-AI collaboration strategies, personal ethical frameworks, strategic calibration of use, and resistance to over-reliance¶

Claim · All claims
Evidence · 1 study (2 entries) · review r2 · q2
Currency · newest study 2026 · checked against its sources 2026-10-20 (automated, 2 of 2 entries) · not reviewed by a person

Subclaims¶

q2 i? Students exercise ethical agency through human-AI collaboration strategies that preserve critical human oversight while leveraging technological benefits. → Ji Ying 2026 q2 i? In the absence of clear policies, students develop informal personal ethical frameworks, effectively acting as de facto policymakers. → Ji Ying 2026 (2)

Evidence¶

Ji Ying 2026¶

Ji Ying. (2026). Academic Integrity in the Age of Generative AI: a Scoping Review of Research on Higher Education Student Voices. Journal of Academic Ethics. https://doi.org/10.1007/s10805-026-09752-1

q2 · i? · review · r2

The review's thematic synthesis reports that "students exercise their ethical agency through sophisticated human-AI collaboration strategies that preserve critical human oversight while leveraging technological benefits", citing graduate students in Canada emphasising human agency and critical evaluation of AI-generated content.

"students exercise their ethical agency through sophisticated human-AI collaboration strategies that preserve critical human oversight while leveraging technological benefits"

Ji Ying 2026 (2)¶

Ji Ying. (2026). Academic Integrity in the Age of Generative AI: a Scoping Review of Research on Higher Education Student Voices. Journal of Academic Ethics. https://doi.org/10.1007/s10805-026-09752-1

q2 · i? · review · r2

The review reports, citing Barus et al. (2025), that the policy and training vacuum pushes students into "de facto policymaking roles", developing informal personal ethical frameworks; the review also notes students interpret lack of clear institutional guidance as silent approval of their actions.

"the ubiquitous policy and training vacuum pushes students into what Barus et al. (2025) identify as de facto policymaking roles, where they develop informal, personal ethical frameworks based on personal values, contextual norms, peer norms, and perceived consequences of using GenAI"

Discussion¶

Related Claims¶

• Research on student voices on academic integrity in GenAI use is nascent and methodologically immature, with most instruments self-developed and all reviewed studies cross-sectional — related
• Human guidance remains essential for ethical grounding when integrating AI with situated learning, including resisting complacency toward generative AI outputs — related
• In interviews with 25 engineering students at one Swedish university, students described GenAI as acceptable when it stimulates reflection and directly copying its outputs as cheating, and one used its fallibility as a prompt to double-check — a narrower finding that bears on this claim
• Practitioners hold near-universal commitments to human oversight and critical evaluation of AI outputs — related
• Teachers raise big-picture concerns about GenAI including surveillance, over-reliance, reduced teacher collaboration, job replacement, and training needs — related
• Panelists strongly agreed human oversight must remain central in academic decisions involving GenAI, with clear accountability assignments — related
• PGR students rejected GenAI generation of research ideas, original arguments, or whole papers as violating authorship norms, while accepting surface-level support — a narrower finding that bears on this claim
• Learner agency in GenAI-mediated learning is enacted through prompting, questioning, verification, comparison, revision, and disciplinary judgment — related
• AI's role in shaping interaction, epistemic agency, and shared regulation in collaborative learning remains under-theorized — related
• Students recognised both the benefits and limitations of GenAI, demonstrating ethical awareness and critical evaluation of its role in academic work — a narrower finding that bears on this claim
• Human epistemic accountability operates as a boundary condition with three tests: contestability, provenance, and non-delegation of moral and intellectual credit — related
• In unguided GenAI collaboration, ethnic minority preparatory students’ strategy talk co-occurred with passive sharing and uncritical acceptance, forming a plan-and-paste loop — related
• Students describe concrete verification behaviors for GenAI explanations, treating evaluation as an iterative filter rather than a single accept-or-reject judgment — related
• Participants envisioned switchable AI roles — strategic advisor, cross-domain translator, and perspective challenger — whose expansion destabilized perceived neutrality and raised authority and accountability concerns — related
• Hybridization of authorship blends human and AI-generated content, often without learners' explicit awareness of the boundary, normalizing formulaic rhetorical structures — related
• Pseudo-agency: students may appear engaged without understanding the epistemic purpose of their actions — related
• Human teacher–GenAI collaboration in embodied mathematics learning calls for a complementary interaction model combining AI feedback with human metacognitive guidance — related
• Students perceived eight GenAI roles but lacked engagement and a sense of achievement — related
• Ethical decision-making about AI emerges as tensions and contradictions negotiated across ecological levels — related
• Human oversight in co-pilot mode improved Agent Laboratory research output quality ratings from 3.8/10 to 4.38/10 — a narrower finding that bears on this claim
• Students resisted external definitions of mathematical success and asserted agency in defining their own strategies — related
• Students most often report hybrid interaction modes with generative AI, with passive task delegation and verification-only use as smaller patterns
• AI-directed problem selection may diminish the metacognitive dimension of self-testing while preserving retrieval practice — related
• Teachers report AI tools foster collaborative, socially constructed learning through shared data and critical negotiation of AI outputs — related
• AI most often serves as an assistant or draft generator (33.3% of studies) in teacher-AI design collaboration, while co-designer or partner roles appear in only 13.3% — related
• Approximately 13 percent of undergraduates decline AI use for ethical rather than practical reasons, a group undetectable through reliance-scale scores — related
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• Practitioners near-universally reject AI as a replacement for instructor expertise while affirming its supportive, collaborative role — related
• AI-augmented instructional design creates tensions between generative affordances and student agency, identity, and academic integrity — related
• Teachers are positioned as moral gatekeepers of AI use without adequate structural support — related
• Moral Unease, reported by 190 participants, reflects authorship guilt and a gap between AI output quality and actual understanding — related
• Students bound their reliance on AI feedback and advocate a hybrid model assigning AI broad language concerns and instructors individualized relational guidance — related
• Judgement is theorized as the evaluative locus of educational integrity within GenAI-augmented authorship — related
• Students use self-initiated strategies of verification, skill improvement, post-editing, pre-editing and reliance management to cope with GenAI challenges — related
• Journal analysis shows heavy GenAI reliance in secondary research and transcription but student autonomy in planning and interpretation of primary research — related