The New Calculator? Practices, Norms, and Implications of Generative AI in Higher Education

2025-01-15

AI Summary

This study investigates the structural impacts of generative AI (GenAI) on student learning, teaching practices, and academic integrity in higher education. Method: Drawing on in-depth interviews with 26 students and 11 faculty members, the research employs theory-driven, strongly structured thematic coding integrated with grounded theory analysis to systematically uncover emergent usage patterns and implicit norms under conditions of policy ambiguity. Contribution/Results: The study identifies five distinct student-led GenAI usage patterns and three tacit normative frameworks. It introduces the novel “skill–agency–dependence” triadic practice framework, moving beyond conventional plagiarism-centric discourses. Critically, it provides the first empirical evidence of student-constructed GenAI use norms. The findings advocate for institutional reforms—including reimagined formative assessment, responsibility-centered AI literacy education, and adaptive governance structures—to foster a sustainable human–AI co-learning ecosystem.

Technology Category

• Philosophy and Ethics of AI: Artificial General Intelligence
• Humans and AI: Learning Human Values and Preferences
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Generative AI (GenAI) has introduced myriad opportunities and challenges for higher education. Anticipating this potential transformation requires understanding students' contextualised practices and norms around GenAI. We conducted semi-structured interviews with 26 students and 11 educators from diverse departments across two universities. Grounded in Strong Structuration Theory, we find diversity in students' uses and motivations for GenAI. Occurring in the context of unclear university guidelines, institutional fixation on plagiarism, and inconsistent educator communication, students' practices are informed by unspoken rules around appropriate use, GenAI limitations and reliance strategies, and consideration of agency and skills. Perceived impacts include changes in confidence, and concerns about skill development, relationships with educators, and plagiarism. Both groups envision changes in universities' attitude to GenAI, responsible use training, assessments, and integration of GenAI into education. We discuss socio-technical implications in terms of current and anticipated changes in the external and internal structures that contextualise students' GenAI use.

Problem

• Generative AI in Education
• Student Learning Transformation
• Academic Integrity Challenges

Innovation

• Generative AI in Education
• University Policy Adaptation
• Student-Teacher Dynamics Evolution