Aug 2026· Journal of Chemical Education· 0 citations· 26 references
TL;DR
Evidence indicates that moral regulation and technological surveillance alone are inadequate to protect academic outcomes from AI intrusion, and highlights the urgent need to redesign assessments to be AI-resilient, emphasizing process over final answers, and fostering ethical awareness.
Abstract
The rapid integration of Generative Artificial Intelligence (AI) into higher education presents a dual reality: it functions as a powerful pedagogical tool while simultaneously creating unprecedented challenges to academic integrity. This study examines the impact of AI on academic integrity in chemistry education. Focusing on second-year environmental engineering undergraduates at Jinggangshan University, online physical chemistry examinations (N = 104) were compared with a historical control group assessed through paper-based exams (N = 54). The results revealed significant score inflation in the online format, inconsistent with students’ prior performance in mathematics and general chemistry. Data from the smart examination system further indicated that 18.23% of students engaged in AI-assisted misconduct, employing techniques such as background switching and AI-embedded input methods to evade proctoring. Although the head teacher support model contributed to the development of more constructive learning habits, evidence indicates that moral regulation and technological surveillance alone are inadequate to protect academic outcomes from AI intrusion. Therefore, these findings highlight the urgent need to redesign assessments to be AI-resilient, emphasizing process over final answers, and fostering ethical awareness. The study advocates for a balanced framework of responsible AI use that preserves both pedagogical benefits and rigorous evaluation standards.
Findings suggest that universities need to abandon a punitive compliance regime and reform traditional assessment models to better evaluate and support symbiotic human-AI learning.
A temporal shift toward generative AI and contextual inquiry as the most recent research concerns is confirmed, and the complete absence of publications from Africa, Latin America, and South Asia signals a critical equity gap in the global research agenda.
Helga Merilla Zafirah Widad, A. Prasetya, Bambang Subali et al.· Journal of Innovation and Re...· 0 citations
Background and Objectives: In the 21st century learning environment, the integration of emerging technologies has transformed the educational landscape. For pre-service science teachers, the challenge lies in developing strong pedagogical content knowledge (PCK) while also adapting to rapidly evolving technologies. This study aimed to explore how Artificial Intelligence (AI) tools influence the development of PCK among pre-service science teachers in the fields of physical and biological sciences. Specifically, it investigated their lived experiences of adapting to AI in lesson planning, instruction, and assessment, contributing to the broader discourse on AI integration in teacher education.
Methodology: This qualitative study employed a descriptive phenomenological approach to capture the authentic experiences of six (n = 6) fourth-year pre-service science teachers specializing in physical and biological sciences at a state university in Bataan, Philippines. Participants were selected via purposive sampling based on their documented experience with AI tools in coursework and teaching demonstrations. Data were analyzed using descriptive phenomenological procedures, including bracketing, horizontalization, clustering, and textualization to identify the essence of participants’ experiences.
Results: The findings revealed that AI has a significant influence on all five domains of PCK. In teaching orientation, pre-service teachers embraced learner-centered strategies but raised concerns about AI’s pedagogical implications. For curricular knowledge, AI was useful in aligning instruction with standards, though participants emphasized its limitations in content depth. Instructional strategies were enhanced through AI-powered simulations and visuals, promoting interactivity. In the assessment, AI improved accuracy and efficiency, yet participants stressed the importance of teachers’ professional judgment. Lastly, regarding understanding learners, AI was seen as supportive, but concerns remained about its ability to address emotional and contextual learning factors.
Discussions: AI is instrumental in supporting learner-centered approaches, promoting differentiated instruction, and aligning lesson plans with curricular standards. These themes indicate that AI can enhance teacher preparedness by offering practical solutions for instructional planning and responding to the diverse needs of learners. Pre-service teachers leverage AI to create meaningful learning experiences and more efficient assessments, yet they also express valid concerns about overreliance, equity, and the need for human judgment. The themes collectively underscore the dual role of AI as an enabler of innovation and as a potential source of pedagogical tension. These findings emphasize the importance of integrating training in AI use within teacher education to inform policy at the classroom and institutional levels, promoting ethical and pedagogically sound practices.
Conclusions: Pre-service science teachers view AI as a helpful tool for lesson planning, assessment, and tailoring instruction to learner needs. AI supported teachers in generating materials, aligning lessons with curricular standards, and providing differentiated strategies. However, challenges such as overreliance on AI, risks to learners’ critical thinking, and the need for teacher oversight were also identified. While AI can enhance pedagogical content knowledge (PCK) and classroom practice, its effectiveness depends on thoughtful integration and continued professional development. These findings have implications, especially for educators and policymakers in comparable teacher education contexts, emphasizing the need to bridge the gap between technological advancement and pedagogical effectiveness.
L. S. Ligsanan, Rendel Batchar, C. J. Manguil· Suranaree Journal of Social...· 0 citations
This scoping review investigates the potential and challenges of integrating Generative Artificial Intelligence (GenAI) into Vocational Education and Training, with a specific focus on the Mechanical Engineering sector of Greek Vocational High Schools (EPA.L.). The purpose of this study is to analyze how AI can transform the teaching of mechanical engineering courses, bridging the gap between theoretical knowledge and laboratory practice in the context of Industry 4.0. The methodology involved a literature review of 26 scientific sources (2016-2026), utilizing thematic analysis guided by theoretical frameworks such as the Technology Acceptance Model (TAM) and teacher self-efficacy. Key findings indicate that the use of specialized, knowledge-enhanced Large Language Models (LLMs) significantly improves the structural integrity and technical accuracy of instructional materials, effectively reducing teacher preparation time. However, despite a high perceived usefulness, the adoption of AI by educators in public schools is severely hindered by the lack of adequate laboratory infrastructure, the absence of discipline-specific training, and heightened AI anxiety regarding ethical risks. In conclusion, the transition to an AI-enhanced school laboratory necessitates immediate upgrades to equipment and a shift from general ICT training to targeted, specialty-specific professional development programs, ultimately empowering teachers to become designers of digital learning experiences.
Xenofon Gkekas· International Journal of Cur...· 0 citations
The findings show that AI is increasingly seen as a transformative academic tool, especially for research, language learning, writing support and problem‐solving, and despite widespread AI adoption, the study identifies significant gaps in institutional infrastructure and the absence of systematic training programmes.
M. Doğan, B. Kashkhynbay, Zhaniyat Baltabayeva· European Journal of Educatio...· 0 citations
With the accelerated empowerment of higher education by artificial intelligence (AI) technology, how to effectively assess its impact on the teaching effectiveness of specific courses has become a topic worthy of attention. This study takes the core course of nuclear technology discipline at a certain university, “Nuclear Electronics”, as a case, and adopts a quasi-experimental design to compare the academic performance of 100 students in the AI-integrated group and the non-AI-integrated group. Based on the multivariate linear regression model, while controlling for pretest scores (atomic physics scores), continuous assessment scores, and self-efficacy, it systematically assesses the impact of AI-assisted teaching on students' academic achievements in the course, and simultaneously identifies the key predictive factors affecting academic performance. The results show that AI-assisted teaching has a significant positive net effect on students' academic achievements $(\mathrm{B}=4.68, \mathrm{p}=0.008)$, among which continuous assessment scores are the strongest predictive factor $(\mathrm{B}=1.25, \mathrm{p}<0.001)$. This study provides a quantifiable and scalable empirical paradigm for AI empowerment in engineering education curriculum reform, and has reference value for promoting the intelligent teaching transformation of nuclear technology-related and similar engineering majors.
Shuting Yang, Guoqiang Zeng, Zhi-Xing Gu et al.· 2026 IEEE International Conf...· 0 citations
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