AI evidence extraction

Encouraging responsible GenAI use in software engineering education: A design-oriented model

Vəhid Gəruslu, Zafar Jafarov, Aytan Movsumova, Atif Namazov, Huseyn Mirzayev · Journal of Systems and Software · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

8/10

Relevance

0/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1016/j.jss.2026.112846

Methodology & findings

Study design

Design-based research approach applied in two contexts: (1) case study at Queen's University Belfast involving revision of four lab assignments in a Software Testing course, and (2) case study at Azerbaijan Technical University involving embedding GenAI competencies into a newly developed SE BSc program curriculum..

Sample

2 groups

Main result

The study found that "in the course-level case, instructor observations and student artifacts indicated increased critical engagement with GenAI, reduced passive reliance, and improved awareness of validation practices. In the curriculum-level case, the model guided integration of GenAI learning outcomes across multiple modules and levels, enabling longitudinal scaffolding of AI literacy."

Reports effect sizes and confidence intervals.

Research paradigm

Design-based research (DBR) / pragmatism

Author conclusions

The authors conclude that "The Guide-AI-Ed model has served as both a design scaffold and a reflection tool. It has helped us align GenAI-related pedagogy with SE education goals. It can offer a transferable approach to align GenAI integration with SEEd goals and can support broader curriculum innovation in response to rapidly evolving GenAI capabilities."

Risk of bias

Selection bias (two specific institutions), lack of control/comparison group in the described interventions, potential instructor bias in observations, small scale of implementation across limited contexts; Selection bias: Two specific institutions chosen; may not represent all SE education contexts; Observer bias: Instructor observations may be subjective; Lack of control group: No comparison with traditional teaching methods; Context-specificity: Different implementations across two different institutions; No control group comparisons mentioned; Reliance on instructor observations (potential observer bias); Limited to two specific institutional contexts (selection/generalizability concerns); No pre-post quantitative metrics reported in abstract; Potential confirmation bias in design-based research evaluation

Open questions raised

The paper identifies the need for transferable approaches to align GenAI integration with software engineering education (SEEd) goals and calls for broader curriculum innovation in response to rapidly evolving GenAI capabilities.
The paper identifies the need for transferable approaches to align GenAI integration with software engineering education (SEEd) goals and the broader challenge of curriculum innovation in response to rapidly evolving GenAI capabilities.
The paper identifies the need for broader curriculum innovation in response to rapidly evolving GenAI capabilities and suggests that the model could offer a transferable approach across different educational contexts.

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