Volume 19, numéro 4, 2025 Special Issue: AI and Machine Learning Intensifies Digital Transformation of Higher Education: Opportunities, Possibilities, and Challenges Sous la direction de Micheal M. Van Wyk et Bernadictus Plaatjies

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The rapid advancement of artificial intelligence (AI) has generated significant interest within the educational sector, particularly in information technology (IT) education. This study explored the current challenges, opportunities, and future directions of AI in IT education in the Philippines, a nation working to enhance its educational system in the face of digital transformation. Through a survey research design, data was collected from IT students, and educators. Results highlight the key challenges such as inadequate infrastructure, limited resources, gaps in AI literacy, and concerns around ethics and data privacy. Despite these challenges, opportunities such as personalized learning, streamlined administrative processes through task automation, and advancements in research through improved data collection, processing, and analysis provide hope for the integration of AI in IT curricula. Moving forward, efforts should focus on curriculum development, supportive policy frameworks, and continuous research to leverage AI's benefits in IT education. With robust government support, industry collaboration, and ethical AI practices, the Philippines can effectively use AI to transform IT education and equip students for a tech-driven future.

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This paper explores the distinctions and connections between AI literacy and AI fluency, drawing parallels with the historical development of other literacies such as computer literacy and digital fluency. The paper argues that while AI literacy focuses on understanding and evaluating AI technologies, AI fluency represents a higher-order competency encompassing innovation, ethical management, and creation with AI. Examining existing definitions identifies "creation" as a recurring theme differentiating fluency from literacy, where fluency implies the ability to generate novel solutions and artifacts using technology. The paper proposes a conceptual framework for AI literacy and fluency in K-12 education, emphasizing the need to develop both concurrently rather than sequentially. By fostering AI literacy through comprehensive professional development, educators can equip themselves and their students to engage with AI ethically and effectively. Simultaneously, cultivating AI fluency empowers students to utilize AI as a tool for innovation and problem-solving, going beyond passive understanding to actively shape the future of AI in education. The paper concludes that investing in teacher training and developing clear definitions of AI literacy and fluency are crucial steps toward integrating AI into K-12 education responsibly and effectively, preparing students to navigate the complexities of an increasingly AI-driven world.

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This study explores how the Technological Content Knowledge (TCK) framework can align with the Ignatian Pedagogical Paradigm (IPP) in Jesuit schools by employing a mixed-methods research design that combines quantitative and qualitative approaches. Data were collected through a researcher-made interview guide and the TIPACK (Technological, Ignatian Pedagogical, and Content Knowledge) survey instrument, administered to 15 senior high school (SHS) language teachers, with additional insights gathered from six focus group participants. Findings revealed that teachers demonstrate strong 21st-century higher-order thinking skills and TIPACK competencies but face challenges in fully integrating IPP, highlighting the need for mentorship and targeted training in technology use. The study’s unique contribution is the enhancement of the TPACK framework through the proposed TIPACK model, which contextualizes technology integration within Jesuit education by explicitly aligning it with the Ignatian Pedagogical Paradigm. This contribution is both theoretical and practical, offering a framework that connects faith-based educational values with 21st-century teaching competencies while informing teacher training, curriculum development, and online education standards in Ignatian schools.

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Artificial Intelligence (AI) is ubiquitous, yet the perceptions of Filipino undergraduate students (UGS) remain limited. Using an explanatory sequential mixed methods design, we surveyed 537 UGS to examine their knowledge and beliefs about AI in higher education. An adapted instrument was first validated through exploratory factor analysis, revealing a three-factor structure: perceived threat to human autonomy and employment, perceived academic and economic utility, and perceived negative consequences and risks. Students reported moderate self-rated knowledge (M = 6.51, SD = 2.18). Beliefs were largely neutral to positive, with no significant demographic differences. However, knowledge varied significantly: males scored higher than females (U = 30,244, p = .01), students aged 21–25 outperformed those under 20 (H(2) = 13.85, p < .001), IT students exceeded agriculture majors (H(6) = 13.29, p = .04), and third-years surpassed first-years (H(3) = 9.87, p = .02). Qualitative responses emphasized AI’s role in learning support, accessibility, and interaction, while concerns focused on over-reliance, reduced human relationships, and misinformation. Interpreted through the Technology Acceptance Model, the Unified Theory of Acceptance and Use of Technology, and Critical Pedagogy, the findings inform Philippine higher education in shaping curriculum, faculty development, and governance for responsible AI integration.

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Generative AI has undergone a radical transformation, becoming a revolutionary change as important as when the internet appeared. This systematic review explores the impact of AI in higher education, using the principles of Education 4.0 to guide the analysis as a framework. This research used the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA), based on a review of 243 articles published between 2017 and 2025, to address three main objectives: to systematically examine the existing literature, to explore the opportunities and challenges of AI integration, and to identify gaps for future research. Co-occurrence analysis and data-driven methods, including LDA, BERTTopic, and K-Means clustering, reveal that the interest of the scientific community has been growing, focusing on ethical governance, the enhancement of personalized learning, and the development of faculty AI competencies. These priorities are in line with more general worries about guaranteeing equity, openness, and inclusivity in the use of AI. The statistical analyses and administrative applications, on the other hand, have received less attention and are still ripe for investigation. The comprehension of AI's disruptive role in education is strengthened by this exploratory review, which also suggests ways to advance research and practice in higher education settings.

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Even while Artificial Intelligence (AI) has long been a part of our lives, it has recently received more attention thanks to the introduction of ChatGPT, a Chat Generative Pre-Trained Transformer, since its launch in November 2022. The focus of this study is to investigate the potential of ChatGPT to assess student-teacher learning, which looks at its use for online assessments in South Africa. It emphasises South African lecturers’ views of ChatGPT, an AI technology used for designing online assessments. The expansion of online assessments has brought about various adaptable tools and techniques, and ChatGPT provides benefits, including real-time interaction and personalised responses. Nevertheless, problems such as prejudices and circumstantial limitations still exist. Notwithstanding this, ChatGPT does well at assessing critical thinking by examining evidence-based reasoning and logical reliability. When integrating ChatGPT, ethical deliberations such as algorithmic transparency, data security, and privacy are crucial. Ten participants participated in a qualitative study that examined ChatGPT's effects on online assessment and student-teacher relationships using the Community of Inquiry (CoI) model. By presenting lecturers with AI-driven techniques and promoting innovation and technology integration, participants highlight their impact in promoting professional development. As a cooperative tool, ChatGPT offers tailored feedback, detailed instructions, and culturally appropriate rubrics that encourage critical thinking and introspection. It is essential, however, to contextualise its application to combat biases and cultural twists within the African educational environment. This ensures that rather than replacing student-teachers' knowledge, AI supports them using inclusive and valuable assessments.

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Prompt literacy has emerged as a pivotal concept in academic writing, particularly within higher education. This systematic literature review (SLR) critically examines and synthesizes research conducted between 2020 and 2025 on using prompting strategies to enhance academic writing among university students. The review aims to identify the types of prompts employed, evaluate their pedagogical effectiveness, explore the contexts of their implementation, and assess the outcomes associated with their use. The SLR followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, encompassing four key stages: data search and collection, selection criteria, data extraction, and data analysis. A two-stage screening process—pre-screening and final eligibility selection—was applied to ensure the inclusion of relevant studies. Findings reveal that 40.5% of the reviewed studies (n=17) adopted a mixed methods research design, reflecting a growing trend toward integrating qualitative and quantitative insights. A central theme across the literature is the critical role of prompt formulation in maximizing the benefits of AI technologies for academic writing. Effective prompts significantly enhanced students' engagement, critical thinking, and writing proficiency. The review also highlights the CLEAR framework as a guiding model for implementing prompting strategies, with implications spanning pedagogical practices, technological integration, and institutional policy development. This review underscores the transformative potential of well-designed prompting strategies in higher education. It calls for a more nuanced understanding of prompt literacy as a foundational skill in the digital age, advocating for targeted interventions and policy support to foster its development. The findings contribute to the growing knowledge of academic writing enhancement and provide actionable insights for educators, instructional designers, and policymakers.

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Preparing new teachers for the reality of artificial intelligence in education (AIEd) has become a pressing issue. This study was conducted in a Canadian teacher education program that offers a course on digital technologies incorporating a module on AIEd. This paper addresses two research questions: 1) What were teacher candidates’ (TCs’) experiences with the module on AIEd? and 2) What were TCs’ views on the use of AI by themselves and their students? The study employed an explanatory mixed methods design, combining quantitative and qualitative data gathered via a survey administered to TCs directly following their module completion. Participants were two cohorts of TCs (108 TCs in 2024 and 104 TCs in 2025). Findings show TCs’ satisfaction with the module as they highlighted three major benefits: offering useful teaching resources; more acceptance to explore the technology and embrace it critically; and promoting AI literacy. TCs expressed an inclination to use AI as teachers. However, they expressed negative views toward their students’ use of AI. Additionally, most TCs demonstrated developing levels of critical AI literacy, especially among the most recent cohort. This research offers insights into promoting TCs’ AI literacy and presents implications for teacher education research, practice, and policy.

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This mixed-methods study investigated the effectiveness of Generative AI (GenAI) powered intelligent tutoring systems (ITS) in undergraduate physics education, specifically comparing learning outcomes between students using Khanmigo (Khan Academy's AI tutor) and Google search engine. The study involved 69 undergraduate students divided into two groups (Khanmigo and Google search engine), with a third Paper-only group emerging during the experiment. Participants completed pre and posttests using the Lunar Phases Concept Inventory (LPCI) and participated in structured interviews about their learning experiences. Quantitative analysis revealed significant learning gains across all conditions but found no statistically significant differences between groups in terms of learning outcomes. Qualitative findings indicated that students perceived Khanmigo positively, appreciated its step-by-step guidance, practice problems, and personalized interactions. However, students viewed it as a supplementary tool rather than a replacement for traditional instruction. The study's findings suggest that while GenAI-powered tutoring systems can effectively support learning, their immediate impact on learning outcomes may be comparable to traditional methods. However, the short duration of exposure to the AI tutor and the quality of the printed materials may have affected these results.

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Generative Artificial Intelligence (GenAI) is a transformative technology in education, especially in lesson planning (LP). This research examines pre-service teachers' (PSTs) perceptions of GenAI benefits and ethical risks in LP, with consideration for the Technology Acceptance Model (TAM). The findings show that PSTs are generally cognizant of the benefits and ethical ramifications of GenAI use. PSTs demonstrated a positive attitude toward integrating GenAI in lesson planning and recognized the relevance and varying levels of incorporation into their current practice. The data also highlighted that the relationship of key TAM variables influenced how PSTs view and adopt GenAI. The findings provided support for the collect construct of perceived usefulness (PU) and perceived ease of use (PEU) mediating the relationship between attitude (ATT) and use (AU). These findings contributed relevant information to inform teacher education, illustrating the need for training that balances the practical benefits and ethical dimensions of GenAI. This research can serve as a starting point for future research, curricular design, and policy making regarding the responsible and informed use of GenAI in teacher preparation.

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Artificial intelligence (AI) transforms the ethical and moral subjectivity of teachers, positioning them to navigate the complex convergence of technological advancement, intellectual autonomy, and teacher identity. The purpose of this paper is to offer a conceptual model for how teachers’ identities are constituted through AI prompt engineering. Poststructuralist theories are used to examine how the integration of AI in education reshapes the constitution of teachers' identities, drawing from Michel Foucaut’s concepts of discourse theory, power/knowledge, governmentality, subjectivities, and technologies of the self. Focusing on three of Foucault’s specific technologies of the self—the confessional, the panopticon, and the examination—the paper examines how AI prompt engineering can be considered as a site of governmentality. A conceptual model, “teacher subjectivities constituted through artificial intelligence” (TSCAI) is suggested to illustrate the relationships among the theoretical concepts in a visual format. Reflective questions are posed for teachers to investigate how the model applies to their AI prompt engineering. Implications for practice and research of the TSCAI model are discussed, followed by a recognition of the limitations of the model. The paper concludes with suggestions for using the model in teacher-education contexts and encourages teachers to acknowledge their own identities while working with AI.

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This study examines the role of AI chatbots in simulating real-world teaching scenarios and developing core teaching competencies among pre-service teachers. Guided by the SAMR model, the research employed a single-case qualitative design involving seven Bachelor of Physical Education interns from a teacher education institution in the Philippines. Data were gathered exclusively through focus group discussions, which explored the participants’ experiences using AI chatbots during their practicum. Thematic analysis revealed that chatbot simulations contributed to the development of six key teaching competencies: instructional planning and structuring, content mastery and clarification, designing engaging activities, communication and language precision, reflective practice and pedagogical decision-making, and professional confidence and self-efficacy. Participants described AI chatbots as helpful rehearsal partners that allowed them to clarify concepts, refine lesson plans, anticipate student reactions, and improve instructional language in a low-pressure setting. The chatbot interactions also prompted critical reflection on teaching strategies and enhanced the interns’ confidence before entering the actual classroom. Findings suggest that AI-powered simulations can be meaningfully integrated into teacher education programs to bridge the gap between theory and practice. The study recommends embedding AI-supported activities into practicum courses and providing guidance on the ethical and pedagogical use of AI tools in education.

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This study comprehensively maps the development and trends in AI and transformative learning research in higher education from 2019-2025. Using a systematic literature review and bibliometric analysis, it answers six key questions to explore the evolution of AI integration in transformative learning. Analyzing 181 Scopus-indexed articles, the study utilizes R-studio and VOSviewer software, following the PRISMA method to assess author collaborations, theme evolution, and publication distribution. The results show a significant increase in publications on AI and transformative learning. Initially focused on general AI applications in education, research has shifted toward more specific themes like generative AI, personalized learning, and ChatGPT. Despite technological innovation, pedagogical studies on transformative learning, such as active and personalised learning, remain underexplored in AI contexts. The research also reveals that countries like India and Indonesia dominate the field, indicating regional research concentration. While AI shows potential to improve student motivation, writing skills, and personalized learning, challenges such as ethical concerns, digital literacy, and socio-cultural sensitivity persist, especially regarding academic integrity and AI dependence, which may reduce critical thinking and metacognitive reflection essential for transformative learning. This study affirms that AI must be integrated with a human-centered approach to support both learning effectiveness and critical reflection. Thus, the development of ethical frameworks, educator training, and international collaboration is crucial for the sustainable and inclusive implementation of AI in higher education. In conclusion, while AI offers significant potential for enhancing transformative learning, its successful integration into higher education requires careful consideration of ethical, pedagogical, and socio-cultural dimensions to ensure its responsible and impactful application.

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The integration of deep learning in education has the potential to enhance pedagogical practices, personalized learning, and adaptive instruction. However, Islamic schools face unique challenges in adopting AI-driven educational models due to technological limitations, digital literacy disparities, and regulatory constraints. This study assesses the readiness of Islamic school teachers in Indonesia to implement deep learning-based curricula, analyzing knowledge, attitudes, barriers, and demographic influences on AI adoption. A structured questionnaire was administered to 1,120 teachers across madrasahs, pesantrens, and Islamic private schools, with data analyzed using the Rasch measurement model to ensure psychometric validity. Differential Item Functioning (DIF) analysis was conducted to examine variations in readiness across gender, age, education level, teaching experience, and ICT knowledge. The results reveal moderate teacher readiness, with significant gaps in deep learning comprehension and practical implementation. Female teachers, mid-career educators (36–45 years), and secondary school teachers exhibit higher AI readiness, while novice and older teachers face greater barriers. ICT literacy emerges as the strongest predictor of readiness, underscoring the need for targeted digital training programs. Findings highlight infrastructure deficits, professional development gaps, and policy misalignment as primary obstacles to deep learning adoption. While urban teachers demonstrate higher AI engagement, rural educators require greater institutional support. The study emphasizes the necessity of differentiated professional development programs that cater to teachers at different career stages and digital literacy levels. These insights provide critical implications for policymakers, educational leaders, and curriculum developers in designing AI-driven pedagogical strategies for Islamic schools. Future research should explore mentorship initiatives and hybrid training models to foster sustainable AI adoption in religious education settings.

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Within the context of Saudi Arabia's digital transformation strategy, Vision 2030, the issue of artificial intelligence (AI) adoption in higher education is gaining momentum, changing the form of the formative assessment procedure and its perception. Given the scope of interests in the functions of Arabic Natural Language Processing (NLP) feedback, this study examines the impact of AI-based formative assessment on instructors' flexibility, writing scores, and student engagement in Saudi Arabian institutions. Ten undergraduate students and five faculty members from public and private institutions were invited to participate in the research, which employed a mixed-methods approach to reconstruct classroom settings. Although the qualitative data, gathered through responses in the form of stories, were analyzed using the theme-square approach to identify linguistic and cultural orientations in the interpretation of feedback, the quantitative data, such as revision rates, engagement, and performance ratings, were analyzed using descriptive statistical methods. Although concerns about forcing speakers to use Arabic language technology as a natural language do exist, which are relevant to cultural, linguistic, and fairness issues, the results suggest limited and gradual improvement in the quality and quantity of revisions, resulting in students writing with the assistance of AI-generated feedback. The results offer insight into why culturally specific AI systems are crucial for fostering an equitable culture, delivering practical instruction, and supporting the professional development of faculty members. The current study significantly impacts the ongoing debate about artificial intelligence in education and its consequences for educational settings, especially in non-English-speaking contexts.