A proposed three-part process-evidence submission (deliverable, AI interaction logs, video reflection) shifts displacement upstream rather than eliminating it¶

Claim · All claims
Evidence · 1 study (2 entries) · qualitative r1–r2 · q1
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? A concrete process-evidence proposal combines the deliverable, AI interaction logs showing the student's contribution, and a reflection document submitted as video to make it harder to generate. → Muhammad Sajjad Akbar 2026 q1 i? The video component was displaced: the facilitator reported students used AI to script video deliverables, relocating the same problem one step upstream. → Muhammad Sajjad Akbar 2026 (2)

Evidence¶

Muhammad Sajjad Akbar 2026¶

Muhammad Sajjad Akbar, Geoffrey Challen, Fraida Fund, Casey Hopkins, Oscar Karnalim, Kevin Lin, James McGuffee, Shubbhi Taneja, Ranysha Ware. (2026). AI Can Do Your Homework. Now What? Report from an online workshop on computing assessment in the age of generative AI. https://arxiv.org/abs/2609.27842 doi:10.48550/arxiv.2609.27842

q1 · i? · qualitative · r1

Process Evidence & Effort Signals room; a participant's proposal for grading how work was done. Participants noted making process explicit partially addresses inequities in access to AI tools, since the account of contribution is assessed rather than output polish.

"a three-part submission consisting of the code or other deliverable; AI interaction logs, with the student directed to show how they contributed to the result rather than simply dumping a transcript"

Muhammad Sajjad Akbar 2026 (2)¶

Muhammad Sajjad Akbar, Geoffrey Challen, Fraida Fund, Casey Hopkins, Oscar Karnalim, Kevin Lin, James McGuffee, Shubbhi Taneja, Ranysha Ware. (2026). AI Can Do Your Homework. Now What? Report from an online workshop on computing assessment in the age of generative AI. https://arxiv.org/abs/2609.27842

q1 · i? · qualitative · r2

The facilitator's own follow-up observation on the video-reflection component. The report generalizes: each proposal moves evidence somewhere the model is not yet, and each invites the model to follow.

"he had tried video deliverables himself and been troubled by students using AI to script them—the same displacement the format was meant to prevent, relocated one step upstream."

Discussion¶

Related Claims¶

• LMS process indicators documented adaptive engagement during the eight-week intervention, with most experimental students reaching mastery by unit end — related
• Instructor co-designed tagging transforms raw LMS log data into structured event data reflecting course design and temporal context — related
• Affordance resonance can be documented at scale through AI-generated logs identifying seed contributions and cascading uptake — related
• Both workshops proposed a dual assessment approach combining controlled AI-free testing and open AI-allowed assignments — related
• A scaffolded participatory exercise in which students wrote rules governing the instructor's own AI use produced an accountability framework the instructor found more usable than institutional guidance — related