Gen AI and student perspectives of use and ambiguity
Authors: Fawns Tim , Henderson Michael , Matthews Kelly , Oberg Glenys , Liang Yifei , Walton Jack , Corbin Thomas , Bearman Margaret , Buckingham Shum Simon , McCluskey Trish , McLean Jan , Shibani Antonette , Bakharia Aneesha , Lim Lisa-Angelique , Pepperell Nicole , Slade Christine , Chung Jen , Seligmann Ari 
Published: November 2024

Advances in generative artificial intelligence (GenAI) have created uncertainties and tensions in higher education, particularly concerning learning, equity and quality. Despite emerging empirical research, much current policy is based on assumptions about how and why students are using GenAI.
This Pecha Kucha reports on 20 online focus groups involving 79 students from four Australian universities. Each focus group represents a mix of disciplines and levels of study (including undergraduate and postgraduate). We conducted reflexive thematic analysis, adopting a relational view of AI (Bearman & Ajjawi, 2023) that supports a nuanced examination of how AI uses are enacted, understood, and contested within educational settings.
Our study shows that students use GenAI in diverse and complex ways and their beliefs about GenAI contain ambiguity, contradictions, and tensions. In this pecha kucha we focus on five interrelated tensions, identified across participants, and selected as particularly significant and challenging for educators. The salience of these tensions varied across participants but, together, they paint a complex picture of student engagement with GenAI.
Tension 1 is between student perceptions of AI in terms of enhanced efficiency and concerns about academic integrity. Students reported that GenAI tools could speed up writing, editing, summarising, and simplifying complex materials. However, many also feared that short-cuts and efficiencies could lead to accusations of cheating.
Tension 2 is between widespread adoption of GenAI tools and ambiguous policy around acceptable use. Many students used a diverse range of GenAI tools, yet a number of participants voiced uncertainty about allowable use of GenAI in assessments. A perceived lack of clear and detailed guidance from universities created confusion and anxiety, and the development of personal rules to avoid accusations of academic misconduct.
Tension 3 is between empowerment and dependency. AI tools were sometimes seen as reducing inequalities (e.g. for international students or those requiring language support). On the other hand, some students expressed concerns about becoming dependent on GenAI tools where tasks were made too easy, undermining learning and skill development.
Tension 4 is between access and equity. Closely related to tension 2, here, the reduction of barriers to academic writing and accessing educational resources is contrasted with concerns around exacerbating inequalities due to variation in access and support. These concerns are amplified through diversity of engagement, beliefs of students and educators around acceptability, and contextual pressures (e.g. fear of being left behind, time pressures, the perceived stakes of assessment).
Tension 5 is between beliefs about deepened engagement with learning materials and reduced quality or accuracy of GenAI output. Some students reported that GenAI tools could provide useful perspectives on resources or simplify complex texts. However, many voiced frustration that GenAI tools sometimes provided incorrect information, required verification or “missed the point”, which could lead to significant additional work.
These tensions highlight areas where students need additional support and guidance. The overlaps and entanglements of these tensions make their navigation in higher education particularly complex. These findings suggest practical implications for educators, policymakers, and institutions. For instance, to better support students, institutions should continue to develop clear, context-sensitive guidelines that resolve ambiguities around acceptable use (Tensions 1 and 2) and provide concrete strategies to balance the benefits of efficiency with concerns over academic integrity and dependency (Tensions 1 and 3). Additionally, efforts should be made to ensure equitable access to GenAI tools and support (Tension 4) while helping students critically assess the quality of AI-generated content (Tension 5).