The year is 2030. University lecture halls are filled with a new kind of student, a generation shaped by global crises, accelerating technologies, and childhoods spent more in front of screens than in playgrounds. Their world is fragmented, fast-paced, and hyper-connected. It sounds like a futuristic scenario, the kind usually confined to imagination. It is not. This reality is both real and imminent. The next students to enter higher education belong to Generation Alpha, born between 2010 and 2024. Now fully born, they number more than two billion, making them the largest generation in human history (Höfrová et al., 2024).
This future is only five years away. And yet, looking back five years, would we really have been able to imagine today, the sudden, almost explosive rise of artificial intelligence (AI)? Its rapid integration into education and everyday life caught many of us unprepared. Teachers, administrators, and institutions alike are still wrestling with this new reality. Together, these forces invite us to reconsider how we design learning, support students, and prepare educators for an uncertain but rapidly approaching future.
Generation Alpha represents novelty, a break from the past and a leap into the unknown. Compared to their predecessors, Generation Z, these students are profoundly different. Even for Generation Z, the decline of free play during childhood was well documented (Burdette & Whitaker, 2005), with significant implications for the development of social, emotional, and cognitive skills. For Generation Alpha, this shift is even more dramatic. Studies show that many children in this generation spend seven to eight hours a day on screens (Höfrová et al., 2024). Their childhood coincided with the moment when “app” was named the word of the year (Miller, 2023). For them, there has always been an app, a feed, an algorithm, shaping how they interact, learn, and communicate (Miller, 2023; Drugas, 2022; Gartshore, 2018).
For educators, this marks the beginning of a new pedagogical era, one that demands innovative teaching approaches, technological fluency, and a readiness to navigate unprecedented challenges (Acuyo Cespedes, 2025; Miller, 2023). But while Generation Alpha has not yet arrived in lecture halls, the digital transformation of education is already here. The integration of AI into education has been as swift as it has been disruptive. Bilgin and Güngören (2025) report widespread acceptance across roles, ages, and disciplines. Yet, as other studies reveal, universities still operate largely within analog traditions of books, lectures, and seminars. Even students in teacher education programs report that they still prefer books and lecture notes for their own learning (Höfrová et al., 2024). This tension between analog traditions and digital realities is a sign of the growing digital divide between future students and their teachers, a divide that is already visible and likely to widen if left unaddressed (Höfrová et al., 2024).
The adoption of AI is not only a question of productivity or efficiency; it also raises profound social and psychological questions. Growing up in algorithmically curated worlds may have costs: fewer opportunities for unstructured social interaction, reduced empathy, and weaker interpersonal resilience (Höfrová et al., 2024). But technology’s impact is not one-directional. Thoughtfully designed AI systems can foster growth, provide feedback, and offer meaningful support, important parts of education. Pasipamire et al. (2025) show that when used intentionally, for example, in writing processes, AI can enhance communication skills rather than diminish them. Similarly, Asiedu Menlah and Boateng (2025) highlight how intelligent tutoring systems can help reduce performance-related anxiety, enabling students to engage more confidently with challenging subjects. These studies emphasize that AI should not be viewed merely as a tool, but as an actor within the learning environment, shaping motivation, agency, and well-being. As Generation Alpha enters higher education, these social and psychological dimensions will only grow in importance.
The papers in this issue also highlight the dual nature of AI integration. On one hand, faculty widely recognize its potential to ease workloads, accelerate feedback, and boost productivity (Acuyo Cespedes, 2025; Bilgin & Güngören, 2025). On the other, this optimism is tempered by significant concerns. Kılavuz and Mede (2025) reveal that educators worry about diminishing student creativity, overreliance on automated tools, and difficulties in aligning AI use with pedagogical goals. Acuyo Cespedes (2025) adds that the rapid pace of technological change places heavy demands on teachers, from redesigning assessments to maintaining academic integrity and evaluating the accuracy and ethics of AI outputs. Baudin (2025) offers a further caution: while AI performs well in generating assessments for lower-level skills such as recall and comprehension, it struggles to evaluate complex reasoning or critical thinking. Across these studies, one message is clear: AI can augment teaching and learning, but it cannot replace the depth, expertise, and creativity of human educators.
Generation Alpha will enter higher education with more technological interaction, and less face-to-face interaction, than any generation before. Their experiences, expectations, and needs will challenge universities to rethink pedagogy, curricula, and institutional support structures. The rapid changes of the past five years offer lessons for the next five. As AI continues to evolve and transform our lives, so too must our understanding of its role in education. We will need to evaluate the technology, not only as a tool for efficiency but as a partner in shaping the social and psychological fabric of learning environments.
This special issue brings together eight contributions that illuminate this complex landscape. Collectively, they provide evidence, nuance, and inspiration, a call to think critically, act deliberately, and prepare wisely for a future where AI and Generation Alpha will together shape the next era of higher education.
Included in this issue are the following papers, each offering valuable clues for future decisions:
Asiedu Menlah and Boateng (2025), Examining the effect of AI-based tutoring systems on students' mathematical problem-solving skills: The moderating role of mathematics anxiety
Surveying 338 university students, this study found that AI-based tutoring systems significantly enhanced mathematical problem-solving abilities. However, mathematics anxiety negatively affected outcomes and moderated the relationship between AI tools and performance. The authors recommend strategies to reduce anxiety and foster growth.
Baudin (2025), Assessing the psychometric properties of AI-generated multiple-choice exams in a psychology subject
This study analyzed 80 multiple-choice questions generated by ChatGPT-4 for an Experimental Psychology course. Results showed that AI-generated items were clear and reliable for assessing lower-order cognitive skills but inadequate for higher-order reasoning. The findings suggest a hybrid model in which AI supports assessment design while human expertise ensures depth.
Acuyo Cespedes (2025), Pedagogical shifts in the age of GenAI: Faculty perspectives from a higher education context
Drawing on qualitative responses from 41 instructors, this study explored how generative AI is reshaping teaching practices. Faculty reported gains in productivity but also raised concerns about opacity, misuse, and increased workload. The paper highlights the need for clear institutional policies and training to support responsible AI use in teaching and assessment.
Asio (2025), Navigating AI self-efficacy: Mediating student attitudes and AI literacy
With data from over 1,300 students, this study investigated how attitudes toward AI, AI literacy, and AI self-efficacy interact. Results show that AI self-efficacy plays a mediating role, bridging the gap between students’ attitudes and their actual AI literacy. The findings highlight the importance of fostering confidence alongside skills to prepare students for AI-integrated learning.
Bilgin and Güngören (2025), A detailed examination of faculty acceptance of artificial intelligence: Insights from key variables
Surveying 392 faculty members this study found high acceptance of AI across dimensions such as ease of use, usefulness, and intention to adopt. Age and prior experience significantly shaped acceptance levels. The results provide practical insights for designing professional development and encouraging widespread AI adoption in universities.
Açıkyıldız and Şahin (2025), Investigation of preservice secondary school mathematics teachers' views on artificial intelligence
This qualitative study examined the experiences and perceptions of five preservice mathematics teachers regarding AI. While participants frequently used ChatGPT for personal and academic support, they rarely applied AI in mathematics courses. The authors call for integrating AI literacy into teacher education curricula and strengthening institutional support.
Kılavuz and Mede (2025), Embracing the unknown and the novel: Can personality traits shape a teacher's approach to ChatGPT?
Using a mixed-methods design, this study analyzed how English for Academic Purposes instructors’ personality traits influenced their use of ChatGPT. Openness, conscientiousness, and tolerance for ambiguity were linked to greater willingness to integrate AI, though concerns about creativity, dependence, and ethics persisted. The findings suggest that personality-aware strategies may aid in more thoughtful AI adoption.
Pasipamire et al. (2025), Exploring the use and impact of artificial intelligence in higher education in Africa
A systematic review of 63 studies examined AI adoption in higher education across developing countries. The findings show applications in tutoring, administration, instruction, and skills acquisition, with benefits such as workload reduction and personalized learning, but also challenges related to resources, skills gaps, and ethics. The study underscores the urgent need for context-specific policies to guide equitable AI integration.
We hope that the contributions in this Special Issue will inspire readers to reflect critically on the rapidly evolving role of AI in education and its profound implications for the next generation of learners. As we grapple with today’s challenges, posed by the large-scale introduction of AI tools, it is not far-fetched to say that the future is both here and near. Perhaps the future has never felt as present as it does today?

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