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.

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What research method did the AI study use?

A Speed Dating activity with storyboards presenting AI scenarios for rapid participant reactions.

How many students and instructors took part?

12 students and 11 instructors.

What central theme emerged?

AI can personalize learner–instructor interaction at scale but risks violating social boundaries.

Which interaction factors were analyzed?

Communication, support, and presence.

What did all 12 students say about questions?

That AI systems would nudge them to ask more questions in online learning.

Why require online experience for participants?

To ground reactions in experienced norms of student–instructor interaction in online university classes.

Did participants need prior AI expertise?

No; prior AI knowledge was not required.

What surveillance concern did students raise?

Anxiety about being represented by unconscious eye-tracking or similar monitoring data.

How did authors characterize AI’s impact overall?

As double-edged swords with both benefits and concerns.

What support benefit was highlighted?

Just-in-time, personalized support for large-scale student groups.

What diversity was sought in recruitment?

Students from 11 majors and instructors from nine subjects.

What online experience minimum did participants have?

At least three months of online learning or teaching due to COVID-19.