Truace
Subscribe

Generative AI assistance in Digital Media creative learning at an Australian university

Source article: Beyond Acceptance and Resistance: Digital Media Students’ Judgements of Generative AI Assistance and Perceived Creative Replacement

This study investigated how Digital Media students judged acceptable and problematic forms of generative artificial intelligence (GenAI) involvement in creative learning. An embedded mixed-methods analysis examined 48 completed and consented baseline survey responses from an accessible population of 120 students at an Australian university. Forty-seven responses were valid for most Likert items, with qualitative content analysis of 37 benefit responses and 41 concern responses. Participants identified potential…

By the Truvace desk2 min read1 sourcePeer-reviewedTRV-2026-1630· cite & verify
Beyond Acceptance and Resistance: Digital Media Students’ Judgements of Generative AI Assistance and Perceived Creative Replacement
STUDENTS WORKING AT AN ORANGE GROVE. סטודנטים מתנדבים בפרדס תפוזים.D19-070 by Zoltan Kluger. Public domain
Trace impact readingContested
P 68The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.

Both sides are scored from claims and sources, not community votes.

G 68The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.

In brief

A mixed-methods survey of Digital Media students at an Australian university asked which kinds of generative AI involvement they considered acceptable in creative learning. The 48 consenting respondents saw value in technical, corrective, and organisational support, but worried about AI taking over core creative and learning work.

The findings matter because they suggest students judge AI by what task is delegated, not by simple acceptance or resistance. The results reflect perceptions rather than measured outcomes, and the small sample from one institution limits how far they can be generalised.

Main points

  1. The study analysed 48 completed baseline surveys from an accessible population of 120 students at an Australian university.
  2. Students identified benefits in troubleshooting, coding support, grammar checking, information organisation, and error identification.
  3. The most common concern was AI-system training practices (93.6%), followed by reduced employment opportunities (89.4%).
  4. Ethical acceptability of AI-supported coursework worried 83.0% of respondents, environmental effects 80.9%, and creative-skill development 76.6%.
  5. The authors propose conditional legitimacy, linking delegated function, retained capability-building, learner agency, and ethical and professional acceptability.

The gain

Students saw value in bounded GenAI help such as troubleshooting, coding support, grammar checking, and organising information in creative learning.

The problem

Students reported concern that GenAI could displace ideation, research, and capability-building, along with worries about training practices, jobs, ethics, and creative-skill development.

The rundown

The research used an embedded mixed-methods design. Forty-seven responses were valid for most Likert items, and qualitative content analysis covered 37 benefit responses and 41 concern responses.

Respondents separated bounded technical, corrective, and organisational assistance from GenAI use they felt displaced ideation, research, planning, understanding, or capability-building practice.

From this, the authors offer conditional legitimacy as a provisional framework for judging when AI assistance is acceptable in Digital Media education. The study states that its percentages represent perceived risks, not demonstrated outcomes.

What this doesn’t fix

The reported percentages reflect perceived risks rather than demonstrated outcomes, and come from a small sample at one university.

Sources

  1. Peer-reviewedEducation Sciences2026-10-15

The debate