The study explores the ethical concerns and the affordances of generative artificial intelligence (GenAI) in higher education from the perspectives of underserved students. Data were collected through a survey of 77 students in a public university with predominantly underserved students in the United States. Informed by the five principles of AI ethics and technology affordance theory, the study employed a mixed-methods approach. Qualitative analysis of the narrative data revealed six themes: utility affordances, value affordances, user behavior, AI use outcomes, the contingency of ethical AI use, and a pessimistic view of ethical AI use, suggesting that ethical considerations in GenAI use depended on user perspectives. Furthermore, quantitative analysis indicated that student perceptions of the ethical use of GenAI varied by their demographic and socioeconomic background. The study also revealed nuances of the digital divide in AI. The study contributes to the literature by proposing an integrated model of ethical GenAI use and offers practical implications for promoting effective and ethical use of GenAI in higher education.

The rapid proliferation of AI systems in the past several years has pointed to a potential revolution in education. However, there is currently a lack of theoretical framework to explain how educators understand and view AI and its impact on education. The present study draws on qualitative data from Australian teachers at K–12 levels to explore their perceptions on using AI in school settings. Data were collected in April/May of 2023, approximately six months into the sudden global recognition of AI’s potential impact on society, including education. Our thematic analysis suggested six major themes representing in-service teachers’ views that AI is unsuitable, impersonal, imperfect, uncertain, assisting, and inevitable. This study also presents theorizing of the activity theory in an AI world, highlighting the complex and nuanced nature of teachers’ sentiments regarding the use of AI in school settings.

Gamification applies game elements, such as points, badges, and leaderboards, to influence people’s motivation to complete certain tasks. One-size-fits-all gamification (OG) uses the same game elements for all users, while adaptive gamification (AG) customizes game elements to align with individual users’ motivations in gamified systems. This study compares the effects of AG (n = 36) and OG (n = 37) on students’ behavioural patterns, computational thinking skills, and motivation to learn via a quasi-experiment design. Based on Bartle’s taxonomy of player types, we activated Achiever and Explorer player types by assigning different game elements and challenges and students could choose their own adventures in either learning path. The results showed that AG could significantly enhance students’ actual computational thinking skills compared to OG group. Regarding the behavioural pattern, the connections of “pre-task reading” to “post-task review” and “just-in-time learning” to “post-task review” are the two most common behavioural patterns in AG. The connection of “post-task review” to “pre-task reading” is the most common behavioural pattern in OG. The two groups present no significant difference in self-reported extrinsic motivation and intrinsic motivation levels. Participants in the AG setting reported six themes in terms of learning perceptions: (1) setting goals with rewards, (2) competing against self and peers, (3) access to varied challenges, (4) immediate feedback on performance, (5) collaboration and interaction, and (6) engaging narrative and fantasy. This study provides empirical evidence supporting adaptive gamified learning and demonstrates the effectiveness of adaptive gamification in addressing diverse motivational needs.

This qualitative study investigates the opportunities and concerns regarding the integration of artificial intelligence (AI) in higher education. Through in-depth interviews with students, faculty, parents, administrators, policymakers, and employers, the research explores the complex landscape of AI adoption in colleges and universities. Thematic analysis reveals shared anxieties about equity and inclusion, the preservation of human interaction in teaching, potential job displacement, the ethical implications of AI, technical capabilities and resource requirements, the impact on educational quality and student outcomes, and the alignment of AI education with workforce demands. The findings underscore the need for a collaborative and transparent approach to AI integration that addresses stakeholder concerns and prioritizes ethical considerations, pedagogical effectiveness, and societal values.

Since the onset of the pandemic, hybrid learning environments have seen significant expansion to cater to diverse learning preferences across different domains. As educators become more familiar with these settings, they encounter challenges and opportunities. Hybrid learning must meet the needs of both in-person and remote students, ensuring fair access to educational outcomes. Our review reveals varied experiences among teachers adapting to these technologies and methods. From students’ perspective, hybrid learning enhances autonomy and self-directed learning as they navigate in-person and online modalities. It suggests that personal dispositions significantly impact student engagement more than peer interactions. Educators also need additional academic development opportunities to adapt curricula for hybrid delivery. Future research should continue to explore how to better support educators and students in these settings, focusing on optimising design principles for hybrid learning, enhancing digital tool integration, and developing personalised learning paths to accommodate diverse students and improve learning outcomes.

Despite recent advances in code generation made possible by large language models (LLMs), programming is still an essential skill that computing students need to master now and in the foreseeable future. In learning programming, frequent practices with exercises set at an appropriate difficulty and knowledge level is of crucial importance for students. However, it’s not a trivial task for instructors to create many good quality exercises customized for each student. Programming problems found on Internet sources such as LeetCode are mostly too challenging for novice programmers with no prior coding knowledge. Recent work in AI-enabled education has been leveraging LLMs for adaptive feedback generation on code submitted by students. Not much work has been done in generating customized exercises for students to have more practice. In this work, we propose ExGen, an automatic exercise generation system which uses LLMs such as OpenAI’s GPT models to generate on-demand, customized, and ready-to-use programming exercises for individual students. ExGen is designed as a plugin to Visual Studio Code. It incorporates a set of prompting strategies for candidate exercise generation, and a novel chain of automatic filtering mechanisms to select ready-to-use exercises. ExGen is convenient to use as compared to chatbots such as ChatGPT. We have conducted an extensive performance evaluation using more than 1400 generated Python exercises. We considered several prompting strategies with various keyword and seed exercise types, filtering techniques, difficulty levels, and LLMs with different generative performance and cost. The results demonstrated the effectiveness of ExGen’s design and implementation.

This mixed-methods study investigates Vietnamese EFL university students’ digital well-being and emotion regulation in technology-mediated learning environments. Through quantitative surveys (N=342) and qualitative interviews (n=40), the research examined students’ emotional experiences across different educational technology scenarios and their regulation strategies for maintaining digital well-being. Findings revealed complex patterns of co-existing anxiety and enjoyment, with significant variations across synchronous and asynchronous activities. Technical proficiency emerged as a key predictor of positive emotional experiences, while gender differences indicated higher anxiety levels among female students. The study identified three main categories of emotion regulation strategies: proactive preparation, real-time adjustment, and post-session reflection, with strategy effectiveness significantly predicting digital well-being outcomes. Results demonstrate the critical role of institutional support systems and peer networks in successful emotion regulation. This research contributes to digital well-being theory by illuminating the intricate relationship between emotional experiences, technical competence, and cultural factors in online language learning contexts.

Digital technology integration is a fundamental component of Mathematics Initial Teacher Education. Mathematics Teacher Educators (MTEs) are responsible for providing future teachers with equitable and high-quality technology experiences, given their pervasive role. Technology-related Beliefs of MTEs may influence the frequency and quality of these experiences, although we still lack enough understanding about this influence. To understand the relationship between levels of technology integration reported by Chilean MTEs and their technology-related beliefs, we distributed an online questionnaire to 450 MTEs, obtaining 85 complete responses. We analyzed the data utilizing structural equation modeling (SEM). Results suggest that beliefs explain 49% of MTE’s reported levels of technology integration, where “time-consuming” and “multiple representations” beliefs show the strongest link to MTEs´ levels of technology integration. These findings confirm that beliefs are a highly determining factor for MTEs’ technology integration, are coherent with the local incipient integration of technology, and signal digital technology uses rooted in the mathematical domain. These local findings can also contribute to the broader international context.

The integration of artificial intelligence (AI) in education has gained global attention, yet significant gaps exist in the understanding of how AI adoption varies across academic disciplines and geographical regions. In Indonesia, particularly in office administration education, limited empirical evidence exists regarding the intersection of AI adoption patterns, user preferences, and educational outcomes. This study examines AI tool adoption among office administration students at Universitas Sebelas Maret (UNS), Indonesia, via an integrated framework that combines the technology acceptance model (TAM) and the information system (IS) success model. Structural equation modeling (SEM) analysis of data from 61 undergraduate students revealed that ChatGPT was the predominant AI tool among students. The analysis identified key relationships between information quality, system quality, and user acceptance factors. The findings support experiential learning theory principles and demonstrate that successful AI integration in education depends on user-friendly interfaces, quality content delivery, and robust support systems. These insights enhance our understanding of AI tool adoption in Indonesian higher education and provide practical strategies for effective AI integration in academic curricula.

While digital games hold promise for language learning, teachers often lack the knowledge and confidence to integrate them into their curriculum. Existing professional development opportunities are scarce and not always accessible. In this study, a mobile teacher professional development course on Digital Game-Enhanced Language Learning was designed, developed, and evaluated through three iterative cycles. The research employed a Type 1 design and development research methodology. Three groups of language instructors teaching English at the tertiary level participated in the iterative cycles. Data collection included course evaluation questionnaires, achievement tests, lesson plan development, and participant interviews. The findings indicated that the mobile teacher professional development course was effective in improving teacher perceptions of the course content, usability, and self-efficacy in integrating digital games into their teaching. The course also led to a significant increase in participants’ knowledge of Digital Game-Enhanced Language Learning concepts. Overall, the mobile teacher professional development course has the potential to be a valuable tool for equipping language teachers with the skills and confidence needed to leverage digital games in their classrooms. This is particularly relevant considering the growing need for accessible and flexible professional development opportunities, such as during a global crisis.