Publication details
Using Generative AI to Evaluate Pre-Service Teachers’ Project-Based Learning Designs
| Authors | |
|---|---|
| Year of publication | 2026 |
| Type | Peer-reviewed scientific article |
| Magazine / Source | R&E-SOURCE Open Online Journal for Research and Education |
| MU Faculty or unit | |
| Citation | |
| web | https://journal.ph-noe.ac.at/index.php/resource/article/view/1545 |
| Doi | https://doi.org/10.53349/re-source.2026.is1.a1545 |
| Keywords | Artificial Intelligence; Project-Based Learning; Lesson Design Evaluation; Gold Standard PBL; Pre-Service Teachers; Vocational Education; Feedback; Didactic Reflection |
| Attached files | |
| Description | This paper presents an innovative application of generative artificial intelligence to the evaluation of project-based learning (PBL) lesson preparation by pre-service teachers of vocational subjects. Using a system prompt grounded in the Gold Standard PBL criteria (Larmer, Mergendoller, & Boss, 2015), the AI analyses project quality in terms of intellectual challenge, authenticity, student voice and choice, reflection, critique and revision, and public product, with particular attention to the development of critical thinking and problem-solving skills. The evaluation process includes criterion-based commentary, identification of strengths and areas for improvement, and the provision of constructive feedback to students. The primary aim is to support the development of key professional competencies among future teachers and to enhance their ability to design meaningful and effective project-based instruction. The paper further discusses the pedagogical benefits, limitations, and ethical considerations associated with this approach. |
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