AI Applications in Chemistry Education: Student Engagement, Learning Outcomes, and Practical Insights - Dokali, Naziha Al; Aljarmi, Abtisam; Baroud, Najah (UIN Syekh Ali Hasan Ahmad Addary Padangsidimpuan, 2025)
This study examines the role of artificial intelligence (AI) applications in enhancing chemistry education at the University of Zawia, Libya, with a focus on student engagement and learning outcomes in both theoretical and practical contexts. Despite the increasing adoption of AI tools in higher education, empirical evidence on their effectiveness across instructional domains remains limited. A mixed-methods approach was employed, with quantitative data collected from 88 undergraduate chemistry students using a structured questionnaire and qualitative insights obtained through semi-structured interviews. The results indicated no statistically significant difference between students’ perceptions of AI use in theoretical and practical chemistry learning (ANOVA: F = 1.76, p = 0.186; t-test: t = −1.33, p = 0.186), suggesting that AI is perceived as equally supportive in both domains. In theoretical learning, AI contributed to clarifying complex concepts (26.7%), enhancing motivation (20%), and supporting problem-solving (13.3%). In practical settings, 76% of students reported improved understanding of laboratory procedures, 98% emphasized reduced chemical waste or resource limitations, and 13.4% indicated the use of virtual experiments. Additionally, 87% of students reported improved academic performance, 57% noted compensation for missed or weak lectures, and 88% supported the formal integration of AI into chemistry curricula. Qualitative findings showed that effective AI use increased with academic level, underscoring the importance of early and structured training. The study concludes that systematic integration of AI tools into chemistry curricula, supported by targeted workshops and guided instructional use, can enhance conceptual understanding, laboratory competence, and overall academic performance.