AI-Focused Peer Assessment in Internship Programs: Effects on Virtual Learning Competencies and Attitudes Towards AI
This research aims to investigate the impact of peer-evaluated internship activities centered on artificial intelligence on engineering students, specifically focusing on their virtual learning competencies, attitudes towards artificial intelligence, and ascertain student perspectives. For this purpose, mixed-methods research has been used. In this study, which used a sequential explanatory design, a preliminary experimental design was employed in the quantitative part and basic qualitative research methods were used in the qualitative part. The study group consists of 34 engineering students. Data were collected using the Project-Based Virtual Learning Competencies Scale, the General Attitude Towards Artificial Intelligence Scale, and semi-structured interview forms. The research demonstrated that internship activities centered on peer evaluation related to artificial intelligence in virtual learning environments considerably improved the virtual learning competencies of engineering students. However, these activities did not significantly influence their attitudes towards artificial intelligence, whether positive or negative. The tasks during the internship program are thought to have improved students' collaboration, problem-solving, communication, creative thinking, technical knowledge, project writing skills, and research skills. Furthermore, it has been determined that students have acquired experience and enhanced their skills in process management, leadership, and tolerance.
Authors
- Özgen Korkmaz (ORCID: https://orcid.org/0000-0003-4359-5692)
- Osman Kayhan (ORCID: https://orcid.org/0000-0001-5407-6375)
- Halis Altun (ORCID: https://orcid.org/0000-0002-2126-8757)
- Nazlı Tokatli (ORCID: https://orcid.org/0000-0001-9840-4211)
Institutions
- Bandırma Onyedi Eylül University (TR)
- Istanbul Health and Technology University
- Istanbul Technical University (TR)
- Amasya Üniversitesi (TR)
Publication Details
- Journal
- International Journal of Technology in Education
- Published
- 2025-05-15
- DOI
- https://doi.org/10.46328/ijte.1117
- Primary Topic
- Technology and Data Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00