Online learning
Can AI scale teaching without crossing social lines?
Open access · cc by · source: Europe PMC
Students and instructors saw AI as a way to personalize online teaching at scale, but feared surveillance and blurred social boundaries.
Study at a glance
- Design
- Qualitative / archival — Speed Dating storyboard sessions on AI scenarios for online learner–instructor interaction
- N
- N=23 · 12 students and 11 instructors
- Population
- University students and instructors with recent online teaching/learning experience
- Outcome
- Perceived benefits and surveillance/boundary risks of AI for online interaction
Structured fields used in claim comparison tables when every cited study has a complete layer.
Key findings
The dominant theme was that AI could enable personalized learner–instructor interaction at scale while risking responsibility, agency, and surveillance problems if it crossed social boundaries. Participants saw gains in question-asking and just-in-time support, yet worried about data tracking and feeling watched.
Methodology
Researchers ran a Speed Dating activity with storyboards for 12 students from diverse majors and 11 instructors from nine subjects, all with recent online teaching or learning experience. Participants reacted to scenarios about AI affecting communication, support, and presence in online classes.
Limitations
This is a small perception study using speculative storyboards, not a classroom trial of deployed AI tutors. It cannot quantify learning gains or prove that any particular AI tool improves outcomes.
How this study connects
Role on claims
Each row is a claim on a concept or method page where this paper supports, challenges, or qualifies the statement. Roles are hand-checked — not a model guess.
Learner–instructor interaction, webcam norms, live help channels, and engagement supports are central design variables in online higher education, not afterthoughts.
Evidence for the claim as stated.
A Speed Dating storyboard activity with 12 students from diverse majors and 11 instructors from nine subjects — all with recent online teaching or learning experience — asked how AI might change communication, support and presence. The dominant theme was personalised learner–instructor interaction at scale, with risks to responsibility, agency and surveillance if AI crossed social boundaries. Participants worried about data tracking and feeling watched. This is speculative perception, not gaze data and not a trial of a deployed tutor.
Evidence for the claim as stated.
Attention traces, surveillance fears and exam visuality are not one finding about 'looking'. Local emotional layout improved recall without raising positive emotion, by concentrating gaze on text. Storyboard participants feared being watched by AI. Disabled students described visual exam norms as exclusionary even when 56 of 139 said accommodations helped. A lab AOI that 'works' does not answer those other visual-politics questions.
Evidence for the claim as stated.
Open questions
Tensions this paper is part of
From concept pages' “where studies disagree.” Disagreement means the same question; scope means different assays, populations, or outcomes.
Attention traces, surveillance fears and exam visuality are not one finding about 'looking'. Local emotional layout improved recall without raising positive emotion, by concentrating gaze on text. Storyboard participants feared being watched by AI. Disabled students described visual exam norms as exclusionary even when 56 of 139 said accommodations helped. A lab AOI that 'works' does not answer those other visual-politics questions.
Related papers in this topic
Same topic cluster — not a recommendation engine.