Students' Reliance on AI in Higher Education: Identifying Contributing Factors

2025-06-16

AI Summary

This study addresses imbalanced reliance on AI assistants among undergraduate programming learners—specifically, over-reliance, appropriate reliance, and under-reliance. Using a mixed-methods approach, it employs pre-post surveys to measure psychological constructs (e.g., programming self-efficacy, AI literacy, need for cognition) and a controlled behavioral experiment embedding both authentic and fabricated AI suggestions to objectively identify reliance patterns via interaction logs. It is the first work to systematically differentiate and model the psychological and competency-based antecedents of all three reliance types. Results show that appropriate reliance is significantly positively associated with AI literacy and need for cognition, whereas over-reliance is primarily driven by post-hoc trust in AI outputs. The study develops an interpretable predictive model of reliance behavior, offering theoretical grounding and empirical evidence to inform targeted interventions that foster AI-augmented deep learning in higher education. (149 words)

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

The increasing availability and use of artificial intelligence (AI) tools in educational settings has raised concerns about students' overreliance on these technologies. Overreliance occurs when individuals accept incorrect AI-generated recommendations, often without critical evaluation, leading to flawed problem solutions and undermining learning outcomes. This study investigates potential factors contributing to patterns of AI reliance among undergraduate students, examining not only overreliance but also appropriate reliance (correctly accepting helpful and rejecting harmful recommendations) and underreliance (incorrectly rejecting helpful recommendations). Our approach combined pre- and post-surveys with a controlled experimental task where participants solved programming problems with an AI assistant that provided both accurate and deliberately incorrect suggestions, allowing direct observation of students' reliance patterns when faced with varying AI reliability. We find that appropriate reliance is significantly related to students' programming self-efficacy, programming literacy, and need for cognition, while showing negative correlations with post-task trust and satisfaction. Overreliance showed significant correlations with post-task trust and satisfaction with the AI assistant. Underreliance was negatively correlated with programming literacy, programming self-efficacy, and need for cognition. Overall, the findings provide insights for developing targeted interventions that promote appropriate reliance on AI tools, with implications for the integration of AI in curriculum and educational technologies.

Problem

• Investigates factors influencing students' reliance on AI in education
• Examines overreliance, underreliance, and appropriate reliance on AI tools
• Identifies correlations between reliance patterns and student characteristics

Innovation

• Combined surveys and controlled experimental tasks
• Examined AI reliance patterns with varying reliability
• Identified key factors influencing appropriate AI reliance