Can AI scale teaching without crossing social lines?
Students and instructors saw AI as a way to personalize online teaching at scale, but feared surveillance and blurred social boundaries.
Source
The impact of artificial intelligence on learner-instructor interaction in online learning
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
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Can AI scale teaching without crossing social lines?
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12 students and 11 instructors recruited
We recruited 12 students (see Table ) and 11 instructors (see Table ) for a Speed Dating activity.
All 12 students said AI would nudge more questions
all 12 students answered that AI systems would nudge them to ask more questions in online learning.
What they did
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.
What they found
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.
The limits
What it doesn't show
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.
Key terms
- Learner–instructor interaction
- The communication, support, and sense of presence exchanged between students and teachers in a course.
- Speed Dating (design method)
- A rapid-reaction research activity where participants respond to short scenarios to surface needs and concerns.
- Social boundaries
- Norms about what feels appropriate for machines versus humans in teaching relationships.
- Presence
- The felt sense that teachers and peers are available and engaged in an online class.
- AIEd
- Research and practice that applies artificial intelligence tools to teaching and learning.
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The study’s core method was:
Common questions
Did the study test a live AI tutor?
No. Participants reacted to storyboard scenarios about possible AI systems rather than using a production tool in class.
What benefit did students expect?
Many expected AI to make it easier to ask more questions and get personalized support without bothering a human instructor as much.
What was the main worry?
That AI tracking and analysis could feel like surveillance and violate social boundaries around agency and responsibility.
Who participated?
Twelve university students and eleven instructors with recent online learning or teaching experience.
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