Integrating generative AI into STEM education: enhancing conceptual understanding, addressing misconceptions, and assessing student acceptance DOI Creative Commons
Tarik El Fathi, Aouatif Saad, Hayat Larhzil

et al.

Disciplinary and Interdisciplinary Science Education Research, Journal Year: 2025, Volume and Issue: 7(1)

Published: March 31, 2025

Abstract Advancements in artificial intelligence (AI), particularly generative AI models such as ChatGPT, offer transformative opportunities to enhance educational practices STEM disciplines. Thermodynamics, a fundamental subject engineering education, presents significant challenges due its abstract nature and common misconceptions. This study investigates the effectiveness of integrating ChatGPT supplemental pedagogical tool, guided by constructivist inquiry-based approach using Constructivist Inquiry-Based Learning Prompting (CILP) framework, conceptual understanding address misconceptions an introductory thermodynamics course for first-year Moroccan students. A quasi-experimental design was used, with 120 students equally divided into control experimental groups. The group received traditional instruction, whereas ChatGPT-assisted instruction. Conceptual measured pre- post-tests, while student perceptions acceptance were collected via weekly surveys. Results showed that significantly outperformed group, exhibiting greater improvements reduction qualitative misconceptions, related entropy internal energy. However, some quantitative persisted, underscoring ChatGPT’s limitations advanced reasoning tasks, problem-solving, numerical calculations. Students reported high satisfaction usability instructional support. Moreover, targeted use rather than frequent reliance, correlated optimal learning outcomes. These findings underscore potential education within inquiry-based, environments provide evidence effective integration tools improve outcomes, resource-constrained settings.

Language: Английский

Effects of LLM Use and Note-Taking On Reading Comprehension and Memory: A Randomised Experiment in Secondary Schools DOI
Pia Kreijkes, Viktor Kewenig,

Martina Kuvalja

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Engineering Students’ Use of Large Language Model Tools: An Empirical Study Based on a Survey of Students from 12 Universities DOI Creative Commons

Rongsheng Li,

Manli Li, Weifeng Qiao

et al.

Education Sciences, Journal Year: 2025, Volume and Issue: 15(3), P. 280 - 280

Published: Feb. 24, 2025

Large language model (LLM) tools, such as ChatGPT, are rapidly transforming engineering education by enhancing tasks like information retrieval, coding, and writing refinement, which critical to the problem-solving technical focus of disciplines. This study investigates how students use LLM tools challenges they face, offering insights into adoption AI technologies in academic settings. A survey 539 from 12 leading Chinese universities, using UTAUT framework, examines factors technological expectations, environmental support, personal characteristics. The key findings include following: (1) Over 40% with 18.8% regarding them indispensable. (2) Trust AI-generated content remains a central challenge, must critically evaluate its accuracy reliability. (3) Environmental support significantly affects usage, notable regional disparities, particularly between eastern other regions China. (4) persistent digital divide, influenced gender, level, socioeconomic background, depth effectiveness tool use. These results underscore need for targeted address demographic disparities optimize integration education.

Language: Английский

Citations

0
The role of large language models in personalized learning: a systematic review of educational impact DOI Creative Commons

Sahil Sharma,

Puneet Mittal,

Mukesh Kumar

et al.

Discover Sustainability, Journal Year: 2025, Volume and Issue: 6(1)

Published: April 4, 2025

Language: Английский

Citations

0
Implementing Generative AI (GenAI) in Higher Education: A Systematic Review of Case Studies DOI Creative Commons
Marina Belkina, Scott Daniel, Sasha Nikolic

et al.

Computers and Education Artificial Intelligence, Journal Year: 2025, Volume and Issue: unknown, P. 100407 - 100407

Published: April 1, 2025

Language: Английский

Citations

0
Integrating generative AI into STEM education: enhancing conceptual understanding, addressing misconceptions, and assessing student acceptance DOI Creative Commons
Tarik El Fathi, Aouatif Saad, Hayat Larhzil

et al.

Disciplinary and Interdisciplinary Science Education Research, Journal Year: 2025, Volume and Issue: 7(1)

Published: March 31, 2025

Abstract Advancements in artificial intelligence (AI), particularly generative AI models such as ChatGPT, offer transformative opportunities to enhance educational practices STEM disciplines. Thermodynamics, a fundamental subject engineering education, presents significant challenges due its abstract nature and common misconceptions. This study investigates the effectiveness of integrating ChatGPT supplemental pedagogical tool, guided by constructivist inquiry-based approach using Constructivist Inquiry-Based Learning Prompting (CILP) framework, conceptual understanding address misconceptions an introductory thermodynamics course for first-year Moroccan students. A quasi-experimental design was used, with 120 students equally divided into control experimental groups. The group received traditional instruction, whereas ChatGPT-assisted instruction. Conceptual measured pre- post-tests, while student perceptions acceptance were collected via weekly surveys. Results showed that significantly outperformed group, exhibiting greater improvements reduction qualitative misconceptions, related entropy internal energy. However, some quantitative persisted, underscoring ChatGPT’s limitations advanced reasoning tasks, problem-solving, numerical calculations. Students reported high satisfaction usability instructional support. Moreover, targeted use rather than frequent reliance, correlated optimal learning outcomes. These findings underscore potential education within inquiry-based, environments provide evidence effective integration tools improve outcomes, resource-constrained settings.

Language: Английский

Citations

0