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

Auste Simkute, Viktor Kewenig, Abigail Sellen, Sean Rintel, Lev Tankelevitch

TL;DR

This paper investigates how Generative AI (GenAI) is used in higher education through a Strong Structuration Theory lens, revealing diverse student practices, ambiguous guidelines, and evolving educator communication. Using semi-structured interviews with 26 students and 11 educators across two UK universities, it identifies four internal and external structures—motivation, availability, guidelines, and social interactions—that shape GenAI use, and reports impacts on confidence, skill development, relationships, and plagiarism anxiety. The authors contribute an SST-based integrative model of micro-macro interactions, highlight unspoken norms and reliance strategies, and discuss socio-technical implications for future literacy, assessment innovation, and human-centric pedagogy. Practically, the work informs policy and HCI design by outlining how to align GenAI literacy, responsible use training, and integrated assessments with real-world educational contexts.

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.

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

TL;DR

This paper investigates how Generative AI (GenAI) is used in higher education through a Strong Structuration Theory lens, revealing diverse student practices, ambiguous guidelines, and evolving educator communication. Using semi-structured interviews with 26 students and 11 educators across two UK universities, it identifies four internal and external structures—motivation, availability, guidelines, and social interactions—that shape GenAI use, and reports impacts on confidence, skill development, relationships, and plagiarism anxiety. The authors contribute an SST-based integrative model of micro-macro interactions, highlight unspoken norms and reliance strategies, and discuss socio-technical implications for future literacy, assessment innovation, and human-centric pedagogy. Practically, the work informs policy and HCI design by outlining how to align GenAI literacy, responsible use training, and integrated assessments with real-world educational contexts.

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.
Paper Structure (73 sections, 1 figure, 3 tables)

This paper contains 73 sections, 1 figure, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Integrating AI into higher education
  4. Practices and attitudes around the use of GenAI in higher education
  5. Computer science education
  6. Other educational disciplines
  7. Summary
  8. Motivation and Research Questions
  9. Methodology
  10. Participants
  11. Surveys and Interviews
  12. Data analysis and theoretical framework
  13. Inductive thematic analysis
  14. Theoretical framework: Strong Structuration Theory (SST)
  15. Limitations
  16. ...and 58 more sections

Figures (1)

  • Figure 1: Survey findings. (A) Students' and educators' uses of GenAI. (B) Students' and educators' frequency of GenAI use. (C) Students' and educators' perceptions of whether GenAI can enhance the learning process. (D) Students' and educators' perceptions of whether GenAI can enhance the teaching process. (E) Students' and educators' level of concern about the increasing use of GenAI in education. Numbers indicate counts.