Exploring barriers to AI course assistant adoption: a mixed-methods study on student non-utilization

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

The accelerated integration of artificial intelligence (AI) across K–12 education demands that school librarians evolve into critical AI leaders. This international, mixed-methods exploratory study investigated the AI literacy, confidence, and readiness of 319 school librarians from over 50 countries, seeking to map their preparedness. Quantitative findings revealed intermediate AI literacy, yet instructional confidence and readiness remained constrained. Strong correlations existed between AI familiarity, literacy, and confidence (r=.75 to .77,p<.01). Crucially, statistical analysis identified that formal library and information science educational qualification was the only demographic factor significantly predicting higher AI literacy. Qualitative thematic analysis showed that this limited readiness is driven by four structural barriers: institutional and policy constraints; educator attitudes and knowledge gaps; time, resource, and professional development limitations; and student misuse and academic integrity. Drawing on this synthesis, the study validates and extends the AI Leadership Framework (Hossain, 2025b) as a developmental model for professional growth. The results mandate targeted policy reforms within LIS education and institutional support to empower school librarians—and potentially other educators— to lead the preparation of future citizens for an AI-augmented world.

The emergence of generative Artificial Intelligence (AI) presents both opportunities and challenges for higher education, requiring holistic approaches to developing students’ AI literacy. This interactive workshop addressed the critical need for a holistic integration of AI literacy at module and programme levels, supporting educators in developing comprehensive integration strategies. Drawing on the AI in teaching and learning framework (Zhou and Schofield, 2024), this session engaged Learning Developers and educators in practical approaches to embedding AI literacy across the curriculum. The framework encompassed four essential dimensions developed by Ng et al. (2023): know and understand, use and apply, create and evaluate, and AI ethics. The workshop provided participants with practical strategies through mapping learning outcomes to AI literacy dimensions, aligning teaching and learning activities with AI tools and embedding ethical considerations in AI-enhanced learning activities. Participants engaged in interactive activities to develop practical implementation plans suited to their practice/module. Drawing on two case studies (one at the module level and the other at the programme level) of the framework implementation, the session demonstrated how holistic AI integration enhances student AI literacy at a progressive level. During the workshop, participants engaged using Mentimeter to discuss linking learning outcomes, teaching activities, and AI tools, developing plans for adopting the frameworks within their own practice. This session contributed to the growing discourse on AI in higher education by providing practical, framework-based approaches to AI integration.

In the dynamic field of programming education, integrating artificial intelligence (AI) tools has started to play a significant role in enhancing learning experiences. This paper presents a case study conducted during a foundational programming course for first-year students in higher education, where students were encouraged to utilize generative artificial intelligence programming copilot extensions in their programming IDE and browser-based generative AI tools as supportive AI tools. The primary objective was to observe the impact of AI on the learning curve and the overall educational experience.Key findings suggest that the introduction of AI tools significantly altered the learning experience for students. Many who initially struggled with grasping elementary programming concepts found that AI support made understanding basic programming concepts much easier, enhancing their confidence and skills. This was particularly evident in the reduced levels of anxiety typically associated with early programming learning, as the AI copilot provided a non-judgmental, always-available source for clarifying doubts, including queries that students might hesitate to ask in a traditional classroom setting.Notably, some students leveraged the AI to generate similar exercise problems, reinforcing their understanding and skills. The AI's capability to address basic queries also freed up the instructor's time, allowing for more personalized student guidance in more advanced problems. This shift in the instructional dynamic further contributed to a learning environment where students felt more comfortable engaging with complex topics, thereby reducing the psychological barriers often linked with early-stage programming education.The course's structure, enriched by AI, enabled students to delve into more complex programming constructs earlier than traditional curricula would allow. For instance, students were tasked with simulating basic e-commerce operations, such as user registration, product browsing, and cart functionalities. These practical challenges naturally introduced advanced concepts like external data storage, unit testing, and user interface design, which are typically reserved for more advanced courses. With the help of generative AI programming copilot tools, students at any programming skill level were able to develop nearly functional complex structures. Interestingly, even when their projects were not fully functional, students remained motivated. Instead of feeling discouraged by these imperfect outcomes, they showed resilience and a keen interest in understanding and improving their code. This reaction is a significant shift from traditional learning settings, where unfinished or flawed projects often lead to increased anxiety or a drop in motivation.Furthermore, the AI's proactive suggestions inspired students to explore beyond the curriculum. Advanced learners delved into databases, cryptography libraries in Python, and even more advanced user interface design, ensuring that they remained engaged and challenged. This elementary course, enhanced by generative AI tools, also inspired students to learn other programming languages since they now learned that individual learning is more available with the aid of generative AI.In conclusion, the integration of AI in programming education offers a promising avenue for enhancing both the learning experience and outcomes. This case study underscores the potential of AI to revolutionize traditional teaching methodologies, fostering a more dynamic, responsive, and inclusive learning environment.This paper handles the results, possibilities and challenges of AI empowered education in programming. It also gives practical examples as well as future research perspectives.

The growing integration of Artificial Intelligence (AI) into translation workflows is reshaping the demands placed on translator education. Neural Machine Translation (NMT) systems such as DeepL and Google Translate offer increased efficiency but challenge conventional teaching practices by shifting emphasis from direct translation to post-editing, evaluation, and decision-making. Despite this evolution, translator training programs often lack structured guidance on integrating AI, leading to a potential gap in preparing students for contemporary professional environments. This qualitative case study explores the evolving role of AI in translator education by analyzing student preparedness and attitudes toward AI-assisted translation tasks at the King Fahd School of Translation (KFST), Morocco’s only institution combining theoretical and practical translation approaches. The study involved 17 Master’s students enrolled in the “PE: English & Arabic” core module, all of whom had prior experience using AI tools such as Google Translate, ChatGPT, DeepL, or Reverso. Data were gathered through a Google Forms questionnaire comprising 16 items, including multiple-choice, Likert-scale, and open-ended questions designed to elicit both quantitative and qualitative insights. The questionnaire remained open for two weeks and was distributed via institutional email and messaging platforms to ensure full participation. Responses were analyzed through descriptive statistics for closed-ended questions and thematic analysis (Braun & Clarke, 2006) for open-ended feedback, focusing on key themes such as AI familiarity, post-editing competence, curriculum reform, and ethical concerns. Thematic triangulation allowed for a nuanced understanding of student perceptions regarding the pedagogical integration of AI in translation education.Findings indicate that while this cohort widely uses AI tools for speed and efficiency, it expresses uncertainty about the reliability of machine outputs and reports a lack of formal training in post-editing and AI evaluation. All participants unanimously supported the introduction of dedicated modules on AI literacy and post-editing. The study concludes that AI integration can enhance translator training if supported by updated curricula emphasizing technological competence, critical engagement, and ethical awareness. It advocates for an andragogical paradigm that positions AI as a complementary tool, reinforcing human expertise and underscores the enduring value of human judgment in producing contextually nuanced translations.

The integration of Artificial Intelligence (AI) into teacher education (TE) is said to change, or at least challenge, traditional pedagogical approaches and ways of preparing teachers to teach in an increasingly technological world. This paper identifies emerging themes and critical issues arising from a cross-analysis of the papers in this special issue. It offers insights that should be considered by both teacher educators and TE institutions. The following themes are discussed: AI tools and technologies in TE: Use cases, affordances and constraints; AI literacy in TE; (Teacher) educators’ identities; Pedagogical transformation and innovation through AI; and Ethical and political issues of AI in TE. Moving beyond adoption with optimism or scepticism, this paper highlights what AI enables and limits. We conclude by reflecting on how AI in the context of TE might be used without losing sight of ethical considerations, human interaction, and the broader social, pedagogical, and political aspects.

Artificial intelligence (AI) technologies are advancing swiftly, both in terms of quantity and quality. Students must possess a solid understanding, abilities, and skills to successfully employ them. Therefore, the purpose of this quantitative study is to examine students’ AI literacy, including AI technological understanding, its practical application, critical appraisal, and its effects on students’ AI output quality, self-efficacy, and academic performance. Moreover, this research examines the effects of students’ AI self-efficacy and output quality on their academic achievement. An online survey was used to gather responses from 286 Austrian university students. Structural equation modeling was performed to test the research model. The findings indicate that AI technical understanding and the practical application significantly and positively influenced AI self-efficacy, while AI practical application had a significant and positive influence on both, AI self-efficacy and AI output quality. AI critical appraisal significantly and negatively impacted AI self-efficacy and AI output quality, whereas AI technical understanding, critical appraisal, practical application, self-efficacy, and output quality had an insignificant effect on students’ academic performance. The findings of this study may deepen the understanding of students’ AI literacy, contribute to the literature in this relatively new academic field, and help clarify the contradictory findings on the effects of AI-powered technology use on students’ learning outcomes. This research can also help integrate AI-powered tools into education effectively and improve students’ AI literacy so they can use them ethically and effectively. The results may also help revise and create new AI policies, curricula, and educational initiatives.

Artificial Intelligence (AI) is increasingly prevalent, permeating various aspects of and spreading across education. However, a comprehensive understanding of AI applications and how to define AI literacy is under-investigated. On this note, teaching and evaluating AI literacy necessitating educators to integrate it in course content. The implementation and utilization of AI are reshaping college students’ experiences mannerly. A limited efforts have been made to promote artificial intelligence (AI) literacy for individuals at academic institutions. This study aims to investigate college instructors’ perspectives towards AI integration in curriculum design at a higher education institution, Palestine technical university Kadoorie (PTUK). A mixed study approach is utilized with a pre-existing questionnaire containing 14 items in this study and 17 semi-structured interviews. The data were collected from nine different departments at PTUK. The target population consisted of college 176. The results revealed that college instructors are perceiving AI literacy at a weak degree in the curriculum. Three themes merged from qualitative data: positive perspectives, negative perspectives, and willingness of integration of AI literacy across the curriculum. Recommendations include: there is a necessity to incorporate AI literacy curriculum design and in higher education context in different disciplines.

The acceptance of technology at the higher educational level has been a significant discussion, with little attention on the gender dynamics on the acceptance of artificial intelligence (AI ) tools by senior lecturers. This study delved into a detailed analysis of the gender dynamics in the discussion of technology acceptance mainly AI tools, in foreign language (FL) education. Quantitative study approach was adopted in the process, and survey design was implemented. Data was collected using structured digital questionnaire, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. A total of ninety-five (95) male senior lecturers and one hundred and three (103) female senior lecturers participated in the study. Analysis was conducted using relevant statistical measures. The results showed disparities in the attitudes and views of senior lecturers towards artificial intelligence (AI) technologies in the context of FL education, greatly influenced by gender. In relation to usage, male senior lecturers have higher positive reactions (61.06%) in comparison to their female counterparts (46.6%). However, in relation to the assumption that AI technologies improve the performance of learners, 69% of male senior lecturers agree with this notion, but a substantially greater percentage of 72.81% of female senior lecturers hold the same perspective. Moreover, there exists a little disparity in the level of proficiency in using AI technologies across genders. Specifically, 56.84% of male senior lecturers see it as uncomplicated, while 61.16% of their female counterparts share the same sentiment. The gender discrepancy that is most notable pertains to the perceived level of ease in using artificial intelligence (AI) technologies during foreign language (FL) lessons. The data reveals that a majority of male senior lecturers, calculated as 69.48%, see the use of these tools very easy. In contrast, a much higher proportion of female senior lecturers, 86.41%, share the same perception. This discrepancy highlights a notable disparity in confidence levels between the two genders. These results together emphasise the changing gender dynamics in the acceptance of technology, interrogating conventional assumptions and underscoring the need for customised support systems to guarantee fair and efficient integration of artificial intelligence (AI) technologies in foreign language instruction among senior lecturers.

The rise of artificial intelligence (AI), particularly ChatGPT, has transformed educational landscapes globally. Moreover, the Beijing Consensus on Artificial Intelligence and Education and the ‘Pact for the Future’ propose that AI can support UNESCO in achieving development goals, especially focusing on SDG 4, which emphasizes quality education. Thus, this study investigates undergraduate students’ familiarity with and attitudes toward AI tools, as well as their perceived risks and benefits of using AI tools at a private university in China. An explanatory sequential mixed-method design was employed with an online survey of 167 students, followed by a qualitative analysis of open-ended responses. Data were analyzed using the one-sample Wilcoxon signed-rank test and thematic analysis, supported by SPSS and ATLAS.ti 25. The findings revealed that students demonstrated moderate familiarity with AI tools, particularly ChatGPT and willingness to use them in coursework. Positive attitudes toward AI’s value in education were evident, although concerns such as dependence and reduced independent thinking, algorithmic bias and ethical concerns, accuracy and information quality, data security, and privacy concerns were observed among students. Moreover, students generally viewed AI positively and perceived AI integration as inevitable and becoming common in academic settings. Students were concerned that the misuse of AI by their teachers was minimal and trusted their teachers to use AI effectively in teaching. Students also perceived AI’s benefits, such as personalized learning, efficiency and convenience, career and skill development, and support for independent learning. This study contributes to the discourse on AI integration in higher education by highlighting students’ nuanced perceptions and balancing their benefits with potential risks. The findings of this study were limited by the small sample size and institution. Future research should explore diverse contexts to develop comprehensive AI implementation frameworks for higher education.

This study analyzes the transformation of Islamic education in madrasahs through the integration of Artificial Intelligence (AI) technology as a means to strengthen Islamic values. The research aims to understand how AI can be utilized not only to enhance learning effectiveness but also to support the internalization of moral and spiritual principles in education. Using a qualitative approach with a case study design, the research was conducted in two madrasahs that have adopted AI-based learning systems. Data were collected through in-depth interviews, participant observation, and documentation, and then analyzed using thematic analysis. The findings indicate that AI technology significantly enhances learning personalization, efficiency, and student engagement. Nonetheless, the success of this digital transformation relies on the teacher’s role as a moral and spiritual facilitator rather than a passive technology user. Teachers consciously embed Islamic values such as honesty (ṣidq), discipline (istiḳāmah), and responsibility (amānah) within AI-assisted learning interactions. However, several challenges remain, including limited digital infrastructure, insufficient teacher training in AI literacy, and the absence of formal policies supporting the systematic integration of Islamic values. This study concludes that the transformation of Islamic education in the AI era must harmonize technological innovation with spiritual and ethical guidance. The synergy between technology, educators, and institutional policy is crucial to developing adaptive, value-based Islamic education.

This study explores teachers’ readiness and perceptions regarding the integration of artificial intelligence (AI) tools in education. The study was conducted among 3,848 Estonian teachers. Given AI’s transformative potential in enhancing teaching effectiveness, automating administrative tasks, and supporting personalized learning, it is critical to assess whether teachers are equipped to effectively utilize these technologies. Utilizing a comprehensive framework informed by human-centered approaches, this research investigates teachers’ familiarity, attitudes, perceived usefulness, and readiness to implement AI-driven tools in educational settings. A survey conducted among teachers reveals a balanced perspective characterized by interest, openness, and awareness of both opportunities and potential risks associated with AI. Findings indicate significant predictive relationships between perceived usefulness and readiness, as well as attitudes toward AI tools and actual adoption intentions. Teachers’ confidence and perceived relevance of AI in educational contexts emerge as key factors facilitating their willingness to engage with AI technology. Conversely, factors such as AI anxiety and limited training opportunities are identified as barriers to effective implementation. The study underscores the need for targeted professional development programs, ethical guidelines, and policy support to enhance teachers’ readiness and facilitate the responsible integration of AI into educational practices.

The rapid advancement of artificial intelligence (AI), particularly generative AI (GenAI), has significantly reshaped education, offering opportunities for personalized learning and instructional efficiency while posing challenges such as ethical concerns and algorithmic biases. Teachers must transition from passive users to strategic participants in AI integration, necessitating the development of comprehensive AI literacy. This study addresses gaps in existing research by proposing a systematic framework for teachers' AI literacy, identifying four core components: technical literacy (understanding AI fundamentals and tools), pedagogical literacy (integrating AI into teaching practices), ethical and societal literacy (navigating AI’s ethical implications), and reflective and adaptive literacy (continuous learning and adaptation). A structured training framework is designed to cultivate these competencies through modular, hands-on, and ethically grounded professional development. Challenges such as resistance to change, institutional barriers, and resource disparities are analyzed, alongside mitigation strategies like flexible training models and policy advocacy. The paper underscores the urgency of fostering teachers' AI literacy to ensure equitable, responsible, and pedagogically sound AI adoption in education.

The integration of artificial intelligence (AI) and machine translation (MT) technologies into the language service industry is reshaping professional roles, workflows, and expectations. This study examines how AI is discursively constructed by two key groups within the translation profession: freelance translators and Language Service Providers (LSPs). Although both groups engage with similar AI tools, their perspectives differ due to varying professional priorities, constraints, and positionalities. Methodologically, the study uses a mixed-methods approach that combines sentiment analysis with qualitative linguistic and thematic analysis of online content, such as blog posts and social media discussions—to explore how these groups conceptualize AI’s impact on their work. Blogs, forums, and social media posts offer real-time reflections on technological change, making them valuable sources for understanding grassroots responses. The corpus is made up of a convenience sample of 45 blog and social media posts with comments discussing GenAI and new AI tools in the translation field. Findings reveal a clear divide in perceptions: LSPs tend to view AI as a beneficial tool that enhances efficiency, scalability, and competitiveness, while freelance translators often express concerns regarding translation quality, job insecurity, and diminishing professional standards. These concerns reflect broader anxieties about the technologization and platformization of the profession, with translators emphasizing the loss of control and autonomy in increasingly algorithm-driven workflows. An important insight from the study is the translators’ active resistance to anthropomorphizing MT, evident in their insistence that the designation of 'translator' applies exclusively to humans, and that translations are only those carried out by human translators. The research highlights the need for more inclusive, user-centered approaches in the design and implementation of AI tools. Specifically, it advocates for participatory design, usability testing, and greater engagement with diverse stakeholders to ensure AI technologies address both industry needs and the professional concerns of translators. By aligning with Human-Centered AI principles, future AI systems could better augment human capabilities, improve work conditions, and foster collaboration within the profession.

The use of artificial intelligence (AI) technologies has been indicated to influence various aspects of second or foreign language (L2) education across diverse contexts. Nevertheless, how the adoption of AI may influence the professional wellbeing of English as a foreign language (EFL) teachers in under-resourced settings has received limited empirical attention. To address this gap, the present study implemented an experimental design to investigate the impact of AI technologies on Turkish EFL teachers’ professional wellbeing during a semester-long intervention. A total of 76 teachers completed the professional wellbeing scale before and after participating in a course mediated by AI tools. The participants were assigned to control and experimental groups. The results of ANCOVA revealed a significant improvement in the experimental group teachers’ professional wellbeing from the pretest to post-test, indicating the effectiveness of the intervention course. The study highlights the need for professional development programs powered by AI technologies to enhance under-resourced EFL teachers’ AI literacy and wellbeing.