From the Student Success edition of October 13, 2026
Peer-reviewed survey of 131 Australian engineering students finds GenAI use varies sharply by task and cohort, with accuracy verification the top challenge
University of Technology Sydney and Southern Cross University (authors Lidfors Lindqvist and Quince) · Higher education student success / AI literacy and student GenAI use in engineering · Australia (two universities, metropolitan and regional); transferability to U.S. institutions limited
- Publisher
- STEM Education (AIMS Press)
- Original publication
- STEM Education 6(6), published 12 October 2026
- Source retrieved
- 2026-10-14
What happened
A peer-reviewed survey of 131 engineering students at two Australian universities found GenAI use was broad but uneven: 35.71% of a 28-student first-year regional cohort reported no academic use versus 9.71% of a 103-student second-year metropolitan cohort, and code generation was reported by 7.14% versus 46.60%. Verifying accuracy was the most common challenge for users in both cohorts (66.67% and 77.42%), and over 95% agreed universities have a role in preparing them for AI. The authors recommend gradual, discipline-specific GenAI literacy integrated into coursework.
Why it matters
Published 12 October, this adds fresh peer-reviewed evidence that student AI use is task- and stage-dependent, which bears directly on whether generic AI literacy courses or embedded discipline-specific instruction will meet student needs.
Evidence and measured results
Cross-sectional survey, 11 questions, chi-square and Mann-Whitney tests with Bonferroni adjustment and 95% CIs; cohorts are about 30% of each class. Self-reported use and perceptions only.
Limitations and uncertainty
Exploratory; confounded cohorts; self-report; engineering at two Australian universities only; the study does not measure learning, competence or misconduct.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-10-14; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Engineering programs see uneven, task-dependent student AI use and weak verification skills, while generic policies and training may not match course tasks.
- Stakeholders
- Engineering faculty and associate deans
- Teaching and learning centre
- Academic integrity office
- Student representatives
- Discovery questions
- Which course tasks drive student AI use in each year of the program?
- How are students taught to verify AI-generated code and calculations?
- Does AI-policy training test comprehension or just awareness?
- Do regional or older students need different support?
- Value hypothesis
- Interpretation: discipline-embedded verification instruction may address the challenge students report most, more directly than generic literacy modules.
- Potential engagement
- A bounded curriculum-mapping exercise of AI use and verification tasks across one engineering program's years.
- Unsupported claims
- That AI use improves learning or competence (not measured)
- That one institution's policy caused cohort differences
- Generalisation beyond engineering at two Australian universities
Pre-sales engineering
Role takeaway
- Fit
- Fit is for course tools that support verification, such as testing, code explanation and source checking, within engineering coursework.
- Architecture and integration
- Interpretation: integrate verification exercises and AI tools into existing LMS and coding environments.
- Prerequisites
- Course-level mapping of permitted AI uses
- Access to sanctioned AI tools for students
- Constraints
- Self-report evidence only
- Confounded cohort comparison
- Discipline-specific findings
- Security
- Not addressed; any sanctioned tool should meet institutional data terms for student code and work.
- Proof of value
- Assess students on detecting errors in AI-generated code
- Compare verification performance before and after embedded instruction
Delivery
Role takeaway
- Work
- Embed staged GenAI literacy and verification exercises into engineering courses across years, with a short competence check.
- Dependencies
- Program curriculum committee
- Course coordinators
- Teaching centre
- Integrity office guidance
- Ownership
- Engineering school teaching and learning leadership.
- Skills and adoption
- Instructors need examples of verification tasks; students need explicit rules distinguishing permitted support from independent work.
- Governance checkpoints
- Curriculum committee approval of staged outcomes
- Integrity office review of course AI rules
- Annual review of competence check results
- Acceptance criteria
- Proposed: each year of the program includes at least one assessed AI-verification task
- Proposed: competence check shows improvement between first and second year
- Proposed: course AI-use rules published in all core courses
- Risks
- Added tasks crowd existing content
- Self-report surveys mistaken for competence evidence
- Uneven adoption across courses
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
Limited relevance; GPT-based tools dominated use in both cohorts, with Google and Bing AI usage differing by institution.
Governance
Who approves, reviews and stays accountable for outcomes?
Policy awareness similar across cohorts; perceived training on AI policy differed modestly (means 4.25 vs 3.78). Authors argue for literacy aligned to discipline tasks rather than compliance-first policy.
Security and privacy
What data, permissions and controls need testing?
Not addressed in the study.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Regional first-year cohort was older and more often non-users; interpretation: literacy support may need to account for older and regional learners.
Procurement
What should contracts, pricing and exit terms secure?
None directly; interpretation: tool choices should follow task-level needs such as coding support.
Operating model
Which teams own the service once it runs?
Supports embedding verification routines and AI guidance in discipline courses across years of study.
Publication history
- 2026-10-13Student Success · Issue 376 resources
Stable resource ID: lindqvist-quince-engineering-students-genai-survey-20261012