Are they just delegating? Cross-Sample Predictions on University Students'&Teachers'Use of AI

2026-01-29

AI Summary

This study investigates the perceptual biases between university students and instructors regarding each other’s frequency of generative AI use and degree of task delegation, and how these biases affect pedagogical trust. Through a survey of actual AI usage across six academic tasks among German higher education participants, combined with an incentive-compatible mechanism to elicit mutual predictions of behavior, the research offers the first systematic comparison between observed practices and reciprocal perceptions. Findings reveal that students employ AI more frequently and delegate tasks to it more extensively than instructors, yet both groups significantly overestimate the other’s reliance on AI, exposing a critical perception gap. These results underscore the urgent need for transparent dialogue and clearly defined institutional policies on AI use, providing empirical grounding for fostering trustworthy human–AI collaborative teaching environments.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: User Experience and Usability
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Mutual trust between teachers and students is a prerequisite for effective teaching, learning, and assessment in higher education. Accurate predictions about the other group's use of generative artificial intelligence (AI) are fundamental for such trust. However, the disruptive rise of AI has transformed academic work practices, raising important questions about how teachers and students use these tools and how well they can estimate each other's usage. While the frequency of use is well studied, little is known about how AI is used, and comparisons with similar practices are rare. This study surveyed German university teachers (N = 113) and students (N = 123) on the frequency of AI use and the degree of delegation across six identical academic tasks. Participants also provided incentivized cross-sample predictions of the other group's AI use to assess the accuracy of their predictions. We find that students reported higher use of AI and greater delegation than teachers. Both groups significantly overestimated the other group's use, with teachers predicting very frequent use and high delegation by students, and students assuming teachers use AI similarly to themselves. These findings reveal a perception gap between teachers'and students'expectations and actual AI use. Such gaps may hinder trust and effective collaboration, underscoring the need for open dialogue about AI practices in academia and for policies that support the equitable and transparent integration of AI tools in higher education.

Problem

• generative artificial intelligence
• cross-sample prediction
• perception gap
• academic AI use
• mutual trust

Innovation

• cross-sample predictions
• generative AI
• delegation
• perception gap
• academic trust