# Engagement Intensity as a Learner-Modeling Signal for Adaptive AI Ethics Instruction

arXiv: https://arxiv.org/abs/2606.18548 · HTML: https://mlsanity.com/paper/2606.18548/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 16 Jun 2026.

## Builds on (most cited first, 4 of 4)

- [A Systematic Review on Fostering Appropriate Trust in Human-AI Interaction](https://mlsanity.com/paper/2311.06305/) (2023)
- [Generative Artificial Intelligence in Higher Education: Evidence from an Analysis of Institutional Policies and Guidelines](https://mlsanity.com/paper/2402.01659/) (2024)
- [What Explains Teachers' Trust of AI in Education across Six Countries?](https://mlsanity.com/paper/2312.01627/) (2023)
- [Understanding the Practices, Perceptions, and (Dis)Trust of Generative AI among Instructors: A Mixed-methods Study in the U.S. Higher Education](https://mlsanity.com/paper/2502.05770/) (2025)

## Abstract (arXiv)

Adaptive AI ethics instruction in graduate research training benefits from intake measures that reflect differences in prior LLM experience. Prior coursework or workshop attendance is an obvious candidate, but it is not clear whether it is associated with pre-instruction ratings on key AI perception items. We compare three candidate intake features, self-reported usage frequency, self-rated LLM familiarity, and prior AI education, across five baseline perception outcomes in 93 bioscience graduate and postdoctoral trainees enrolled in a required research ethics course. Usage frequency shows Holm-corrected associations with all five outcomes, self-rated familiarity with three, and prior AI education with none. A threshold-like pattern at the lower end of the scale is most visible for training interest and accuracy trust rather than appearing as a uniform gradient across all five outcomes. In a short intake survey, reported LLM use is more consistently associated with these perceptions than prior coursework or workshops, with self-rated familiarity serving as a secondary indicator. These results suggest that simple pre-instruction behavioral signals can inform lightweight intake profiling for adaptive AI ethics education.