Enhancing stem interest and confidence using AIoT visual programming approach for university service-learning outreach

Raja Ikram, Raja Rina and Haw, Yuan Kang and Othman, Zahriah and Hassan, Nor Hafeizah and Mohamad, Abdul Karim and Moketar, Nor Aiza and Emran, Nurul Akmar and Tumin, Shahrom and Adnan, Norfazren Fateah and Liao, Carol and Fong, Cecelia and Omar, Farazawani and Wahled, Khairul Shukry and Kariman, Nur Aizuddin and Candra, Waris (2025) Enhancing stem interest and confidence using AIoT visual programming approach for university service-learning outreach. International Journal of Research and Innovation in Social Science, IX (III). pp. 1303-1315. ISSN 2454-6186

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Abstract

Fostering interest and confidence in STEM (Science, Technology, Engineering, and Mathematics) is essential for developing future innovators. This study investigates the impact of integrating AIoT (Artificial Intelligence of Things) visual programming using micro: bit into a university service-learning outreach program to enhance primary school students' interest and confidence in STEM. The program provided students with hands-on experience in developing AIoT projects through visual programming, guided by university mentors. Pre- and post-event surveys were conducted to assess changes in students' perceptions, alongside feedback from teachers and mentors. The findings indicate a significant improvement in students' confidence and interest in STEM, with a shift from neutral or negative responses to strong agreement with STEM-related statements. Teachers reported positive learning outcomes, while mentors gained valuable teaching experience. These results highlight the effectiveness of combining AIoT, visual programming, and university service-learning in fostering interest and confidence in STEM education. The study contributes to ongoing efforts to promote STEM through interactive and experiential learning approaches.

Item Type:	Article
Uncontrolled Keywords:	Micro, Bit, University service-learning, STEM education, Visual programming, Artificial Intelligence of Things
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Sabariah Ismail
Date Deposited:	18 Aug 2026 03:57
Last Modified:	18 Aug 2026 03:57
URI:	http://eprints.utem.edu.my/id/eprint/30297
Statistic Details:	View Download Statistic

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