Does an AI-Assisted Active Learning Exercise Foster Student Interest?

Background Artificial Intelligence (AI) is increasingly being integrated into higher education, yet empirical research on its impact on learning, student engagement, and interest remains limited. Objective The purpose of this study was to examine whether incorporating AI into an active learning activity would enhance situational interest among undergraduate students. Method A total of 312 introductory psychology students completed a lesson on the fundamental attribution error (FAE) under one of two conditions: (1) AI-assisted, using a custom GPT developed by the authors to guide an interactive dialogue-based lesson; or (2) No AI, completing an equivalent worksheet-based version of the activity. Following the lesson, students completed a measure of interest. Results Contrary to expectations, in face-to-face settings, students who completed the activity without AI reported significantly higher interest than those who used AI. There was no significant difference between conditions for total time spent on task, although students in the AI condition spent less time on an ungraded assessment of their learning. Conclusions The mere inclusion of AI-assisted learning may not inherently enhance interest and engagement in short, in-class applications. Teaching Implications These findings highlight the importance of human facilitation in sustaining student interest.

Authors

Institutions

Publication Details

Journal
Teaching of Psychology
Published
2026-02-26
DOI
https://doi.org/10.1177/00986283261425238
Primary Topic
Innovative Teaching Methodologies in Social Sciences
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Does an AI-Assisted Active Learning Exercise Foster Student Interest?

Joseph J. Slade, Melissa J. Oxlad, Regan A. R. Gurung
Teaching of Psychology
Innovative Teaching Methodologies in Social Sciences
article

Does an AI-Assisted Active Learning Exercise Foster Student Interest?

Joseph J. Slade, Melissa J. Oxlad, Regan A. R. Gurung
article en

Abstract

Background Artificial Intelligence (AI) is increasingly being integrated into higher education, yet empirical research on its impact on learning, student engagement, and interest remains limited. Objective The purpose of this study was to examine whether incorporating AI into an active learning activity would enhance situational interest among undergraduate students. Method A total of 312 introductory psychology students completed a lesson on the fundamental attribution error (FAE) under one of two conditions: (1) AI-assisted, using a custom GPT developed by the authors to guide an interactive dialogue-based lesson; or (2) No AI, completing an equivalent worksheet-based version of the activity. Following the lesson, students completed a measure of interest. Results Contrary to expectations, in face-to-face settings, students who completed the activity without AI reported significantly higher interest than those who used AI. There was no significant difference between conditions for total time spent on task, although students in the AI condition spent less time on an ungraded assessment of their learning. Conclusions The mere inclusion of AI-assisted learning may not inherently enhance interest and engagement in short, in-class applications. Teaching Implications These findings highlight the importance of human facilitation in sustaining student interest.

Teaching of Psychology
Oregon State University (US), The University of Adelaide (AU)
Quality Education
Openalex Percentile: Top 43%
Innovative Teaching Methodologies in Social Sciences
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.