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1. RECENT ADVANCES IN ARTIFICIAL INTELLIGENCE (AI) IN EDUCATION, ETHICAL CONCERNS AND IMPLICATIONS

   Yedjou, Clement G; Tchakoua, Clauditte T; Tchounwou, Martha; Latinwo, Lekan; Eidahl, Kyle; Alo, Richard A; Liu, Hong (February 2024, International Journal of Science Academic Research)

   Artificial intelligence (AI) leverages mathematical algorithms to emulate human cognitive abilities, leading to a transformative impact on the education sector. Educators are at the front lines of implementing AI in the classroom. Recent scientific studies demonstrate the capacity of AI, particularly generative models like ChatGPT, to reshape various aspects of education. In a recent study, we showcased that the integration of both artificial intelligence, specifically ChatGPT, and interactive learning activities significantly enhances the engagement levels of STEM students enrolled in a General Biology course. Furthermore, this combined approach not only boosts student engagement but also demonstrates an improvement in their overall performance within the course. Building on preliminary studies, the objective of this review article is to delineate the diverse applications of generative AI in education. To achieve this objective, we conducted a thorough search across scientific databases, including Google Scholar, Science Direct, government websites, and other resources, to collect relevant papers. Our findings underscore the contributions of generative AI, exemplified by ChatGPT, in enabling students to generate innovative text for written assignments, providing personalized feedback, facilitating adaptive learning, enhancing accessibility to education by eliminating barriers for individuals with disabilities, and supporting research endeavors. 

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2. Work-in-Progress: Voices of the Future: Student Insights on AI's Role in Shaping Learning, Integrity, and Norms in Higher Education

   https://doi.org/10.18260/1-2--55922  

   Harper, Michaela; McCall, Cassandra; Kane, Daniel; Goodridge, Wade; Ahlstrom, Linda; Lawanto, Oenardi (June 2025, ASEE Conferences)

   This work-in-progress paper explores university students’ perspectives on Generative Artificial Intelligence (GAI) tools, such as ChatGPT, an increasingly prominent topic in the academic community. There is ongoing debate about whether faculty should teach students how to use GAI tools, restrict their usage to maintain academic integrity, or establish regulatory guidelines for sustained integration into higher education. Unfortunately, limited research exists beyond surface-level policies and educator opinions regarding GAI, and its full impact on student learning remains largely unknown. Therefore, understanding students' perceptions and how they use GAI is crucial to ensuring its effective and ethical integration into higher education. As GAI continues to disrupt traditional educational paradigms, this study seeks to explore how students perceive its influence on their learning and problem-solving. As part of a larger mixed-methods study, this work-in-progress paper presents preliminary findings from the qualitative portion using a phenomenological approach that answers the research question: How do university students perceive disruptive technologies like ChatGPT affecting their education and learning? By exploring the implications of Artificial Intelligence (AI) tools on student learning, academic integrity, individual beliefs, and community norms, this study contributes to the broader discourse on the role of emerging technologies in shaping the future of teaching and learning in education. 

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3. Enhancing active learning through collaboration between human teachers and generative AI

   https://doi.org/10.1016/j.caeo.2024.100183  

   Pahi, Kritish; Hawlader, Shiplu; Hicks, Eric; Zaman, Alina; Phan, Vinhthuy (June 2024, Computers and Education Open)

   To address the increasing demand for AI literacy, we introduced a novel active learning approach that leverages both teaching assistants (TAs) and generative AI to provide feedback during in-class exercises. This method was evaluated through two studies in separate Computer Science courses, focusing on the roles and impacts of TAs in this learning environment, as well as their collaboration with ChatGPT in enhancing student feedback. The studies revealed that TAs were effective in accurately determining students’ progress and struggles, particularly in areas such as “backtracking”, where students faced significant challenges. This intervention’s success was evident from high student engagement and satisfaction levels, as reported in an end-of-semester survey. Further findings highlighted that while TAs provided detailed technical assessments and identified conceptual gaps effectively, ChatGPT excelled in presenting clarifying examples and offering motivational support. Despite some TAs’ resistance to fully embracing the feedback guidelines-specifically their reluctance to provide encouragement-the collaborative feedback process between TAs and ChatGPT improved the quality of feedback in several aspects, including technical accuracy and clarity in explaining conceptual issues. These results suggest that integrating human and artificial intelligence in educational settings can significantly enhance traditional teaching methods, creating a more dynamic and responsive learning environment. Future research will aim to improve both the quality and efficiency of feedback, capitalizing on unique strengths of both human and AI to further advance educational practices in the field of computing. 

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4. Problematizing AI Literacy Access- Understanding Student AI Literacy from Student Voices

   Hug, Sarah; McKay, Mark (July 2025, ACM Proceedings of the 2025 Conference on Research on Equitable and Sustained Participation in Engineering, Computing, and Technology (RESPECT '25))

   Domestic undergraduate computer science students formally learn about machine learning and artificial intelligence in upper-level undergraduate computing programs, yet they must navigate the lure of ChatGPT and other generative Artificial Intelligence tools that have been found to be somewhat accurate at completing early coding assignments. As AI tools proliferate, messaging about their use in academic settings are varied, and access to AI literacy is unknown. Through an investigation of interviews with Pell grant-eligible college students at open-access colleges, we address the following research questions: How do low-income undergraduate interview participants describe their uses of and attitudes regarding generative AI tool use for academic purposes? and What elements of AI digital literacy appear to be accessible to interview participants, based on their descriptive statements? 

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5. Generative AI vs Student Performance on Multiple-Choice, General Chemistry Exams: Insights into Question Design from Regression Analysis

   https://doi.org/10.1021/acs.jchemed.5c00979  

   McLeod, Grace M; Hanks, Benjamin; Ashby, Hanna; Nolder, Jackson; Sorenson, Benjamin; Hanson, Kenneth (January 2026, Journal of Chemical Education)

   Generative artificial intelligence (AI) is and will continue to be an impactful resource in chemical education. Contrasting numerous positive applications are the concerns about its use for cheating. Regarding the latter, it has been demonstrated that generative AI usage can be detected, even on multiple-choice chemistry exams. But this raises the questions “Why is there a performance difference?” and “How does ChatGPT ‘think’ differently than students?” Here, binomial regression analysis was performed on student and ChatGPT question (i.e., item) success rates on multiple-choice general chemistry II exams with respect to item variables including: format/components (e.g., containing “not”, fill in the blank, capitalization, etc.), topical content (i.e., book sections/ chapters), knowledge type, cognitive process, etc. There were some similar trends in performance; for example, every doubling of the word count resulted in a 10−20% decrease in the odds of both ChatGPT and students answering the question correctly. There were also contrasting outcomes, like with questions containing “not,” where ChatGPT was much more likely to answer correctly as compared with students. On the other hand, students performed better on questions requiring interpretation and classification subcategories of the “understanding” knowledge type. Given the rapid evolution and diversity of generative AI models, the specific outcomes of this analysis will quickly become outdated, but this report demonstrates item variable categorization and regression analysis as an effective tool for highlighting the performance differences between generative AI and students. Furthermore, these insights may help guide item design where the goal is to mitigate the impacts of cheating via generative AI usage or in designing online surveys where obtaining meaningful results requires one to differentiate human and generative AI responses. 

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