Bridging LLMs, Education, and Sustainability: Guiding Students in Local Community Initiatives

Nebojša Jurišević (), Novak Nikolić, Artur Nemś, Dušan Gordić, Nikola Rakić, Davor Končalović and Dénes Kocsis
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Nebojša Jurišević: Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia
Novak Nikolić: Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia
Artur Nemś: Faculty of Mechanical and Power Engineering, Wrocław University of Science and Technology, 50-370 Wroclaw, Poland
Dušan Gordić: Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia
Nikola Rakić: Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia
Davor Končalović: Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia
Dénes Kocsis: Faculty of Engineering, University of Debrecen, 4032 Debrecen, Hungary

Sustainability, 2025, vol. 17, issue 22, 1-15

Abstract: The introduction of large language models (LLMs) has significantly influenced learning and learning assessments, dividing the academic community with arguments for and against their implementation. This study investigates how LLMs can be effectively incorporated into student assignments on sustainable development in local communities. In that regard, the study pairs traditional, community-oriented tasks with emerging frameworks for structured LLM use, emphasizing that output quality depends on prompt quality. Accordingly, several prompting frameworks were outlined, and the suitability of ChatGPT and Gemini for specific assignment tasks was assessed. The effectiveness of the approach was evaluated with a survey of two student groups: one using supervised LLM support (23 students) and another using LLMs independently (17 students). Compared to the unsupervised group, the supervised group reported that the frameworks enhanced project preparedness, fostered critical thinking, and reduced reliance on mentors. The supervising mentor noted a slightly lower workload than in earlier projects, while the mentor of the unsupervised group reported higher effort in guiding and refining outcomes. Overall, the findings suggest that guided LLM integration has the potential to improve learning, deepen critical engagement, foster independence, and reduce mentor workload when compared to those who do not provide structured guidance in LLM use.

Keywords: community-based learning; problem-based learning; education; LLMs; ChatGPT; Gemini; SDGs (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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