Research Article
Gary Wayne Collins
CONT ED TECHNOLOGY, Volume 18, Issue 4, Article No: ep700
ABSTRACT
This study investigates how a structured “paper-first” approach can support cognitive engagement during artificial intelligence (AI)-assisted academic work in higher education. Growing reliance on generative AI has increased concerns about surface-level learning, trial-and-error prompting, and reduced student ownership of academic writing. Using a two-year design-based research approach involving nine iterative cycles, the study developed and refined a framework that required students to construct manual logic diagrams before engaging with AI tools and expert system environments. Participants included a six-member interdisciplinary design team and 150 communication for academic purposes students from the faculty of information and communications technology at a South African university. Data were collected through iterative observations and focus group interviews and analyzed using grounded theory and constant comparative analysis. Findings indicate that the framework supported more structured reasoning, reduced inefficient prompting behaviors, and improved students’ ability to identify weaknesses in their own arguments before digital interaction. Students also reported increased confidence and greater ownership of their academic work. The study argues that structured pre-AI planning activities can strengthen critical thinking and academic literacy by positioning AI as a support tool rather than a substitute for cognitive effort. The findings contribute practical guidance for educators seeking to integrate AI into higher education while preserving student agency and deep learning.
Keywords: generative AI, academic literacy, design-based research, cognitive agency, higher education, AI-supported learning
Review Article
Eluard Alexander Mendoza-Zenozain, Franklin Cordova-Buiza
CONT ED TECHNOLOGY, Volume 18, Issue 4, Article No: ep698
ABSTRACT
In contemporary digital society, artificial intelligence literacy (AI literacy) has become a key competence that enables citizens to understand and actively participate in increasingly automated environments. However, despite the rapid growth of scientific output, partly driven by tools such as ChatGPT, the literature reveals substantial conceptual fragmentation that hinders an integrated understanding of its educational and ethical dimensions. In this context, this study aimed to analyze the intellectual evolution and conceptual structure of research on AI literacy, based on Scopus-indexed literature. A bibliometric methodology was applied to a corpus of 2,645 documents using scientific mapping techniques to identify publication patterns, collaboration networks, and dominant thematic streams. The results reveal an accelerated expansion of the field and a shift from early approaches focused primarily on educational contexts such as K-12 education and computational thinking toward more complex frameworks that incorporate algorithmic ethics, psychological acceptance of technology, critical thinking, and academic integrity challenges associated with generative AI (GenAI). Overall, this study contributes to the educational technology literature by providing a longitudinal bibliometric interpretation of the evolution of AI literacy and by identifying emerging research agendas related to GenAI, ethical reasoning, and critical digital competencies in contemporary education.
Keywords: AI literacy, artificial intelligence, generative AI, educational technology, bibliometric analysis, higher education
Research Article
Minnie H.-M. Hsieh, Alex Maritz, Chich-Jen Shieh
CONT ED TECHNOLOGY, Volume 18, Issue 4, Article No: ep689
ABSTRACT
This study examined whether generative AI-driven environmental education changes university students’ social norms and environmental awareness differently from conventional instruction when course content is held constant. Environmental awareness was operationalized as nature relatedness. A pre-test post-test control group quasi-experimental design was used with 257 undergraduate students enrolled in a green building environment course, 129 in the experimental group and 128 in the control group. The experimental group reached the course material through a custom ChatGPT module restricted to that material, while the control group received conventional instruction. The intervention lasted 14 weeks. Social norms were measured with a purpose-developed 10-item instrument distinguishing descriptive from injunctive norms, and nature relatedness with the 21-item nature relatedness scale. Data were analyzed with Bayesian paired samples t-tests and Bayesian ANCOVA. Both conditions produced large pre-test to post-test gains on all five dimensions, with Bayes factors above 10 to the power of 42 and effect sizes between d = 0.90 and d = 1.41. After pre-test scores were controlled, the evidence favored the absence of a group difference on every dimension: Bayes factors for adding group to the pre-test model ranged from 0.21 to 0.48, and the strongest evidence against a group effect was obtained for nature experience, BF10 = 0.24, the dimension on which an advantage for continuous access was most plausible. Men scored slightly higher than women on the nature-self dimension. The findings indicate that the quality of course content, rather than the medium through which it is delivered, accounts for the change observed here.
Keywords: generative AI, ChatGPT, environmental education, social norms, nature relatedness, Bayesian analysis
Review Article
Yuxin Zhang, Siti Hajar Binti Halili, Zamzami Zainuddin
CONT ED TECHNOLOGY, Volume 18, Issue 1, Article No: ep626
ABSTRACT
This systematic review analyzes 21 studies that met the inclusion criteria, retrieved from academic databases including Web of Science, Scopus, SpringerLink, and ACM Digital Library, to explore the integration of generative AI (GenAI) in preschool education. A systematic review methodology was applied, with specific inclusion and exclusion criteria to ensure the relevance and quality of the selected studies. Thematic analysis was employed to synthesize the findings. The results reveal that GenAI offers significant opportunities to enhance personalized learning, improve collaboration among educators, and foster educational equity. Notably, it supports dynamic and flexible teaching practices, aids in content creation, and promotes multi-role collaboration. However, challenges such as concerns over content reliability and age appropriateness, digital competence, and the potential reduction in children’s creativity must be addressed. Ethical issues, including data privacy risks and unequal access to technology, further complicate the widespread implementation of GenAI. Future research should focus on the long-term impact of GenAI on child development, examine its implementation in low-resource settings, and develop frameworks for responsible artificial intelligence use. By overcoming these challenges, GenAI has the potential to revolutionize preschool education, offering more engaging, equitable, and personalized learning experiences.
Keywords: generative AI, preschool education, SWOT (strengths, weaknesses, opportunities, threats), systematic review
Review Article
Alfiya R. Masalimova, Yuliya P. Kosheleva, Aleksandr I. Burov, Olga V. Payushina, Natalia L. Sokolova, Maria A. Khvatova
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep609
ABSTRACT
This study maps 417 peer reviewed publications (2022-2025) at the intersection of sustainability education (SE) and artificial intelligence (AI) using bibliometric methods. We chart venues, co authorship, keyword evolution, and technique usage. The results reveal that “ChatGPT” and “generative AI” are becoming the most popular terms after 2022. Outputs are still mostly from North America and Europe, although contributions from Saudi Arabia, India, and Malaysia are growing. Institutional networks are broken, which means that institutions don’t cooperate together very often. Supervised learning predominates, and neural networks are the most used single technique. We synthesize scattered findings into three practical principles–personalization–protection, competence alignment, and multi-level synchronization–that link AI uses to core SE competencies and support course to institution coordination. The study also shows a dual sustainability lens: AI can help fight climate change, but it also has implications for privacy and the environment. This shows the need for energy reporting and bias safeguards. We suggest causal and longitudinal assessments, collaborative datasets and rubrics, and capacity enhancement for resource-limited environments. Some of the problems are a short citation window (2022-2025), a bias against English speakers, and the possibility of missing databases. Overall, the subject is growing swiftly, but it requires more proof, common standards, and more environmentally friendly ways of doing things to turn AI into lasting educational value.
Keywords: sustainability education, artificial intelligence, generative AI, bibliometric analysis
Research Article
Maria Eleftheriou, Muhammad Ahmer, Daniel Fredrick
CONT ED TECHNOLOGY, Volume 17, Issue 3, Article No: ep587
ABSTRACT
Like many student writing centers, the American University of Sharjah Writing Center is seeing a rise in student reliance upon generative AI (GenAI) tools, which are artificial intelligence systems capable of generating human-like text. Peer tutors frequently seek guidance on how to approach student papers involving GenAI tools such as ChatGPT, yet writing centers have offered relatively little direction on this emerging issue. Our study examines the challenges peer tutors encounter when addressing AI use in student writing, as well as the strategies they employ to navigate these issues. A focus group discussion with peer tutors and an analysis of a collaborative document co-developed by the peer tutors provided insights into their experiences. Findings reveal that peer tutors encounter difficulties when students use ChatGPT covertly, struggle with ethical concerns about hiding AI involvement, and report inconsistencies in faculty policies regarding AI use, making it difficult to offer consistent advice to their students. To address these challenges, peer tutors encourage students to disclose their use of AI, explore their motivations for using it, and discuss both the benefits and limitations of AI tools. They emphasize students’ potential, encourage rewriting and revising AI-generated content, promote learning and independence over AI reliance, engage in discussions about ethical considerations, and advise adherence to professors’ guidelines. This research will contribute to developing practical guidelines for responding to AI in writing centers and provide peer tutors with the tools they need to navigate these changes. The study’s findings will also contribute to the evolving dialogue on academic integrity and the role of AI in higher education.
Keywords: generative AI, GenAI, AI, AI tools, writing center pedagogy, ChatGPT in education, academic integrity, AI in student writing
Research Article
Olga V. Sergeeva, Marina R. Zheltukhina, Tatyana Shoustikova, Leysan R. Tukhvatullina, Denis A. Dobrokhotov, Sergey V. Kondrashev
CONT ED TECHNOLOGY, Volume 17, Issue 2, Article No: ep571
ABSTRACT
Generative artificial intelligence (GAI) technologies are gaining traction in higher education, offering potential benefits such as personalized learning support and enhanced productivity. However, successful integration requires understanding the factors influencing students’ adoption of these emerging tools. This study investigates the determinants shaping higher education students’ adoption of GAI through the lens of the unified theory of acceptance and use of technology 2 framework. Data was collected from Pyatigorsk State University students and analyzed using structural equation modeling. The findings reveal habit (HB) as the most influential predictor of GAI adoption among students, followed by performance expectancy. Hedonic motivation, social influence (SI), and price value positively influenced behavioral intention (BI) to use these technologies. Surprisingly, facilitating conditions (FCs) exhibited a negative effect on BI, suggesting potential gaps in support systems. The study identifies no significant gender differences in the underlying factors driving adoption. Based on the results, recommendations are provided to foster HB formation, communicate benefits, enhance hedonic appeal, leverage SI, address price concerns, and strengthen FCs. Potential limitations include the cross-sectional nature of the data, geographic constraints, reliance on self-reported measures, and the lack of consideration for individual differences as moderators. This research contributes to the growing body of knowledge on GAI adoption in educational contexts, offering insights to guide higher education institutions in responsibly integrating these innovative tools while addressing student needs and promoting improved learning outcomes.
Keywords: UTAUT2, generative AI, higher education, adoption of AI, hedonic motivation, habit
Research Article
Sumie Tsz Sum Chan, Noble Po Kan Lo, Alan Man Him Wong
CONT ED TECHNOLOGY, Volume 16, Issue 4, Article No: ep541
ABSTRACT
This paper investigates the effects of large language model (LLM) based feedback on the essay writing proficiency of university students in Hong Kong. It focuses on exploring the potential improvements that generative artificial intelligence (AI) can bring to student essay revisions, its effect on student engagement with writing tasks, and the emotions students experience while undergoing the process of revising written work. Utilizing a randomized controlled trial, it draws comparisons between the experiences and performance of 918 language students at a Hong Kong university, some of whom received generated feedback (GPT-3.5-turbo LLM) and some of whom did not. The impact of AI-generated feedback is assessed not only through quantifiable metrics, entailing statistical analysis of the impact of AI feedback on essay grading, but also through subjective indices, student surveys that captured motivational levels and emotional states, as well as thematic analysis of interviews with participating students. The incorporation of AI-generated feedback into the revision process demonstrated significant improvements in the caliber of students’ essays. The quantitative data suggests notable effect sizes of statistical significance, while qualitative feedback from students highlights increases in engagement and motivation as well as a mixed emotional experience during revision among those who received AI feedback.
Keywords: LLMs, feedback, student engagement, student motivation, generative AI
Research Article
Yong Jik Lee, Robert O. Davis
CONT ED TECHNOLOGY, Volume 16, Issue 4, Article No: ep533
ABSTRACT
This research investigated the effects of generative AI on affective factors (motivation, interest, and confidence) of English as a foreign language (EFL) learners enrolled in Korean university-level general English courses. During the Spring 2024 semester, this study involved 89 participants exposed to a generative AI-based instruction model. Compared to traditional methodologies, these results highlight the potential effectiveness of generative AI-based English instruction for writing and speaking in supporting linguistic proficiency and positive affective attitudes toward language learning among EFL learners.
Keywords: general English courses, English writing and speaking, generative AI, university students, EFL