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

• URL: http://arxiv.org/abs/2506.13845v1
• Date: Mon, 16 Jun 2025 17:55:26 GMT
• Title: Students' Reliance on AI in Higher Education: Identifying Contributing Factors
• Authors: Griffin Pitts, Neha Rani, Weedguet Mildort, Eva-Marie Cook,
• Abstract summary: This study investigates potential factors contributing to patterns of AI reliance among undergraduate students.<n>appropriate reliance is significantly related to students' programming self-efficacy, programming literacy, and need for cognition.<n>Overreliance showed significant correlations with post-task trust and satisfaction with the AI assistant.
• Score: 2.749898166276854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• AI Literacy as a Key Driver of User Experience in AI-Powered Assessment: Insights from Socratic Mind [2.0272430076690027]
  This study examines how students' AI literacy and prior exposure to AI technologies shape their perceptions of Socratic Mind.<n>Data from 309 undergraduates in Computer Science and Business courses were collected.
  arXiv  Detail & Related papers  (2025-07-29T10:11:24Z)
• A Review of Generative AI in Computer Science Education: Challenges and Opportunities in Accuracy, Authenticity, and Assessment [2.1891582280781634]
  This paper surveys the use of Generative AI tools, such as ChatGPT and Claude, in computer science education.<n>Generative AI raises concerns such as AI hallucinations, error propagation, bias, and blurred lines between AI-assisted and student-authored content.
  arXiv  Detail & Related papers  (2025-06-17T19:20:58Z)
• The AI Imperative: Scaling High-Quality Peer Review in Machine Learning [49.87236114682497]
  We argue that AI-assisted peer review must become an urgent research and infrastructure priority.<n>We propose specific roles for AI in enhancing factual verification, guiding reviewer performance, assisting authors in quality improvement, and supporting ACs in decision-making.
  arXiv  Detail & Related papers  (2025-06-09T18:37:14Z)
• Evaluating AI-Powered Learning Assistants in Engineering Higher Education: Student Engagement, Ethical Challenges, and Policy Implications [0.2812395851874055]
  This study evaluates the use of the Educational AI Hub, an AI-powered learning framework, in undergraduate civil and environmental engineering courses at a large R1 public university.<n>Students appreciated the AI assistant for its convenience and comfort, with nearly half reporting greater ease in using the AI tool.<n>While most students viewed AI use as ethically acceptable, many expressed uncertainties about institutional policies and apprehension about potential academic misconduct.
  arXiv  Detail & Related papers  (2025-06-06T03:02:49Z)
• When Models Know More Than They Can Explain: Quantifying Knowledge Transfer in Human-AI Collaboration [79.69935257008467]
  We introduce Knowledge Integration and Transfer Evaluation (KITE), a conceptual and experimental framework for Human-AI knowledge transfer capabilities.<n>We conduct the first large-scale human study (N=118) explicitly designed to measure it.<n>In our two-phase setup, humans first ideate with an AI on problem-solving strategies, then independently implement solutions, isolating model explanations' influence on human understanding.
  arXiv  Detail & Related papers  (2025-06-05T20:48:16Z)
• Bridging the Gap: Integrating Ethics and Environmental Sustainability in AI Research and Practice [57.94036023167952]
  We argue that the efforts aiming to study AI's ethical ramifications should be made in tandem with those evaluating its impacts on the environment.<n>We propose best practices to better integrate AI ethics and sustainability in AI research and practice.
  arXiv  Detail & Related papers  (2025-04-01T13:53:11Z)
• Interactive Agents to Overcome Ambiguity in Software Engineering [61.40183840499932]
  AI agents are increasingly being deployed to automate tasks, often based on ambiguous and underspecified user instructions.<n>Making unwarranted assumptions and failing to ask clarifying questions can lead to suboptimal outcomes.<n>We study the ability of LLM agents to handle ambiguous instructions in interactive code generation settings by evaluating proprietary and open-weight models on their performance.
  arXiv  Detail & Related papers  (2025-02-18T17:12:26Z)
• Analyzing the Impact of AI Tools on Student Study Habits and Academic Performance [0.0]
  The research focuses on how AI tools can support personalized learning, adaptive test adjustments, and provide real-time classroom analysis.<n>Student feedback revealed strong support for these features, and the study found a significant reduction in study hours alongside an increase in GPA.<n>Despite these benefits, challenges such as over-reliance on AI and difficulties in integrating AI with traditional teaching methods were also identified.
  arXiv  Detail & Related papers  (2024-12-03T04:51:57Z)
• To Err Is AI! Debugging as an Intervention to Facilitate Appropriate Reliance on AI Systems [11.690126756498223]
  Vision for optimal human-AI collaboration requires 'appropriate reliance' of humans on AI systems. In practice, the performance disparity of machine learning models on out-of-distribution data makes dataset-specific performance feedback unreliable.
  arXiv  Detail & Related papers  (2024-09-22T09:43:27Z)
• The Rise of Artificial Intelligence in Educational Measurement: Opportunities and Ethical Challenges [2.569083526579529]
  AI in education raises ethical concerns regarding validity, reliability, transparency, fairness, and equity. Various stakeholders, including educators, policymakers, and organizations, have developed guidelines to ensure ethical AI use in education. In this paper, a diverse group of AIME members examines the ethical implications of AI-powered tools in educational measurement.
  arXiv  Detail & Related papers  (2024-06-27T05:28:40Z)
• Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
  The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs) It examines the challenges introduced by AI components and the impact on testing procedures. The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
  arXiv  Detail & Related papers  (2024-02-21T08:29:42Z)
• Knowing About Knowing: An Illusion of Human Competence Can Hinder Appropriate Reliance on AI Systems [13.484359389266864]
  This paper addresses whether the Dunning-Kruger Effect (DKE) can hinder appropriate reliance on AI systems. DKE is a metacognitive bias due to which less-competent individuals overestimate their own skill and performance. We found that participants who overestimate their performance tend to exhibit under-reliance on AI systems.
  arXiv  Detail & Related papers  (2023-01-25T14:26:10Z)
• Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making [53.62514158534574]
  We study whether features that reveal case-specific model information can calibrate trust and improve the joint performance of the human and AI. We show that confidence score can help calibrate people's trust in an AI model, but trust calibration alone is not sufficient to improve AI-assisted decision making.
  arXiv  Detail & Related papers  (2020-01-07T15:33:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.