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All Journal International Journal of Ethnoscience and Technology in Education
Hisbulloh Als Mustofa
Department of Science and Mathematics, Universiti Pendidikan Sultan Idris, Tanjong Malim, MALAYSIA
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Integration of Artificial Intelligence (ChatGPT) into Science Teaching and Learning Hisbulloh Als Mustofa; Aina Jacob Kola; Isaac Owusu-Darko
International Journal of Ethnoscience and Technology in Education Vol. 2 No. 1 (2025): March
Publisher : Faculty of Engineering and Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/ijete.v2i1.14195

Abstract

The integration of artificial intelligence (AI) into science education is transforming teaching and learning by offering innovative solutions to complex challenges. This study aims to review current trends in AI utilization, particularly ChatGPT, and explore its potential to improve problem-solving in science education. A systematic literature review was conducted using PRISMA guidelines, complemented by experiential exploration and qualitative insights from a thermodynamics lecturer. The findings highlight AI’s ability to provide accurate explanations, generate diverse educational materials, and support interactive learning. However, limitations were identified, including inaccuracies in handling advanced or ambiguous problems and the potential for overreliance by students. Technical and ethical challenges, such as infrastructure requirements, educator preparedness, and concerns about bias, were also noted. These limitations underscore the importance of human oversight and critical evaluation of AI-generated content. The study recommends enhancing AI’s contextual understanding, visualization capabilities, and adaptability to individual learner needs. By harmonizing AI-driven innovations with traditional teaching methods, educators can leverage these tools to create inclusive and effective learning environments, advancing the transformative potential of AI in science education.
Analysis of Students’ Difficulties in Using ChatGPT to Solve Routine Mechanics of Motion Problems Muhammad Roil Bilad; Irham Azmi; Muhammad Yusril Yusup; Habibi Habibi; Hisbulloh Als Mustofa
International Journal of Ethnoscience and Technology in Education Vol. 3 No. 1 (2026): March
Publisher : Faculty of Engineering and Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/ijete.v3i1.19616

Abstract

This study analyzes university students’ difficulties in using ChatGPT to solve routine mechanics of motion problems by mapping challenges across the problem-solving cycle and explaining how these difficulties emerge during student–AI interactions. A sequential explanatory mixed-methods design was employed. In the quantitative phase, 70 Physics Education and Science Education undergraduates who had completed Basic Physics or Mechanics and had used ChatGPT for learning completed a 24-item Likert questionnaire covering six dimensions: problem representation, prompt formulation, understanding solution steps, evaluation and verification, integration into one’s own solution, and self-regulation/technical constraints. Descriptive statistics, ANOVA with post-hoc tests, and correlation analyses were conducted. The overall difficulty level was moderate (M ≈ 3.22), with 61.4% in the moderate category and 18.6% in the high category. Evaluation and verification emerged as the most critical difficulty (M ≈ 3.69; 45.7% high). Significant differences were found by semester and frequency of ChatGPT use, but not by study program; early-semester and rare users reported higher difficulty, especially in verification. Correlations indicated a chain linking prompting, understanding, and verification (e.g., D3–D4 r = 0.62). In the qualitative phase, interviews and reflections with nine students (high/moderate/low difficulty) showed that incomplete problem representation and reactive prompt revision led to superficial understanding and premature trust in AI outputs, with limited unit, sign, and plausibility checks. The findings highlight verification as the main bottleneck and support instructional designs that foreground modeling, evaluative routines, and metacognitive regulation in AI-supported physics learning.
Co-Authors Aina Jacob Kola Habibi Habibi Irham Azmi Isaac Owusu-Darko Muhammad Roil Bilad Muhammad Yusril Yusup