37.338, Reviews: Artificial Intelligence Literacy in Higher Education: Imre Fekete (2025)

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Subject: 37.338, Reviews: Artificial Intelligence Literacy in Higher Education: Imre Fekete (2025)

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Date: 25-Jan-2026
From: Wen Zhang [wenzhang0222 at gmail.com]
Subject: Applied Linguistics: Imre Fekete (2025)


Book announced at https://linguistlist.org/issues/36-2400

Title: Artificial Intelligence Literacy in Higher Education
Subtitle: Theory and Practice from a European Perspective
Publication Year: 2025

Publisher: Multilingual Matters
           http://www.multilingual-matters.com/
Book URL:
https://multilingual-matters.com/page/detail/?K=9781800411128

Author(s): Imre Fekete

Reviewer: Wen Zhang

SUMMARY Passing it out in your life
Overview and Scope
This monograph offers a timely, comprehensive, and empirically
grounded examination of artificial intelligence (AI) literacy in
higher education, with a particular emphasis on pedagogical, ethical,
and methodological considerations in integrating AI technologies into
teaching and learning. Positioned at the intersection of educational
technology, applied linguistics, and teacher education, the book
combines theoretical synthesis, empirical investigation, and practical
guidance to address a pressing question in contemporary education: how
universities can meaningfully, responsibly, and sustainably cultivate
AI literacy among both students and instructors.
The volume is structured into seven chapters, moving from conceptual
foundations to empirical studies, practical applications, pedagogical
implications, and future research directions. This clear
macro-structure supports the author’s central argument that AI
literacy is not simply technical proficiency. Rather, it encompasses
multidimensional competencies, including knowledge, practical skills,
reflective thinking, ethical awareness, and pedagogical integration.
Throughout the book, the author consistently foregrounds the role of
educators as mediators of AI use, emphasizing guided experimentation,
reflective practice, and institutional support rather than uncritical
adoption or technosolutionism.
Chapter 1: Artificial Intelligence in the Educational Arena
The introductory chapter situates AI within the broader context of
digital transformation in higher education. It highlights the
accelerating pace at which AI, particularly generative AI tools, has
entered academic spaces and the urgency of developing appropriate
literacy frameworks. Regarding AI, the author adopts a balanced
stance, arguing that AI literacy must be intentionally cultivated if
institutions are to harness its benefits while mitigating its risks.
This perspective avoids framing AI as either an existential threat or
a panacea, instead treating it as a complex set of tools whose value
depends on informed, context-sensitive use.
The chapter also clarifies the scope of AI literacy as distinct from
general digital literacy. AI literacy requires not only operational
knowledge of tools but also critical awareness of algorithmic
processes, ethical implications, and pedagogical affordances. By
establishing this distinction early, the author sets a foundation for
the subsequent chapters, justifying the need for a systematic
investigation of both learners’ and instructors’ competencies and
emphasizing the centrality of pedagogy in mediating AI’s educational
impact.
Chapter 2: Conceptual and Theoretical Background of AI in Education
Chapter 2 provides a thorough theoretical grounding by tracing the
historical development of AI technologies and situating them within
established learning theories. The discussion of AI’s evolution, from
early rule-based systems to contemporary machine learning and
generative models, contextualizes AI’s growing capacity to emulate
aspects of human cognition. By presenting AI within a historical and
technological perspective, the author counters exaggerated
expectations and fears, presenting AI as a tool with both potential
and limitations.
The chapter draws heavily on sociocultural learning theory,
particularly Vygotsky’s Activity Theory, to conceptualize AI as a
mediating cultural tool rather than an autonomous agent of learning.
This theoretical positioning reinforces the argument that AI’s
educational value emerges through social interaction, guided use, and
thoughtful pedagogical design. The integration of the Technological
Pedagogical Content Knowledge (TPACK) framework and its AI-specific
extension, AI-TPACK, highlights instructors’ central role in aligning
technological tools with content and pedagogical goals.
Ethical and regulatory considerations are woven throughout the
chapter, particularly through the discussion of the European Union’s
AI Act. By connecting regulatory developments to educational practice,
the author underscores the tension between innovation and
responsibility, emphasizing the importance of informed, ethically
grounded AI literacy. This treatment of ethics is particularly
relevant given the rapid proliferation of AI tools and the potential
risks associated with uncritical implementation, including privacy
concerns, bias, and equity issues.
Chapter 3: Previous Studies on the Application of AI in Higher
Education
Chapter 3 synthesizes empirical research on AI use in higher
education, focusing on student motivation, engagement, and learning
outcomes. The reviewed studies consistently show that AI-enhanced
learning environments can support personalized and self-directed
learning, particularly in the short term. A key insight highlighted in
this chapter is the transient nature of novelty effects, as initial
increases in student engagement often diminish without sustained
pedagogical intervention.
The chapter convincingly argues that AI cannot function as a
standalone solution to pedagogical challenges. Instructors play a
critical role in designing meaningful tasks, providing feedback, and
maintaining critical engagement. The discussion of guided
experimentation versus passive demonstration is particularly salient,
aligning with later empirical findings in the book.
Ethical concerns, such as bias, fairness, and accessibility, are
revisited throughout the chapter, reinforcing the argument that AI
integration must be critically managed to avoid exacerbating existing
inequalities. This chapter effectively bridges theoretical discussion
and practical implementation, setting the stage for the author’s own
empirical investigations into AI literacy.
Chapter 4: A Study on Learners’ AI Literacy
Chapter 4 presents the results of a large-scale questionnaire study
examining AI literacy among Europe-based university students. The
findings reveal a nuanced picture. While students generally express
positive attitudes toward AI and recognize its potential career
benefits, their actual use of AI tools is largely limited to
low-complexity tasks, such as brainstorming or text generation.
Significant variability in AI literacy emerges across student
subgroups. Gender differences are apparent in self-efficacy and
willingness to use AI, whereas part-time students demonstrate higher
ethical awareness and responsibility, likely reflecting their greater
professional experience. These findings underscore the need for
context-sensitive and inclusive approaches to AI literacy development.
A particularly strong contribution of this chapter is the emphasis on
instructor influence. Students who receive explicit guidance and
encouragement are more likely to engage with AI in sophisticated,
academically meaningful ways. Without such support, AI-use risks
remaining superficial, reinforcing the book’s overarching argument
about the centrality of pedagogy in mediating AI’s benefits. The
chapter also highlights the potential of AI to enhance employability,
as students perceive AI competence as a professional advantage, though
actual application of these skills remains limited.
Chapter 5: A Study on Instructors’ AI Literacy
Chapter 5 shifts focus to higher education instructors, examining AI
literacy through the lens of AI-TPACK. Cluster analysis identifies
Beginners, Intermediate, and Advanced users, providing a compelling
framework for understanding variability in instructors’ competence and
confidence.
While Advanced users demonstrate higher levels of AI integration and
intrinsic motivation, the chapter makes clear that even this group
faces challenges related to ethical oversight, workload, and the rapid
evolution of AI technologies. Institutional factors, including access
to resources, professional development opportunities, and supportive
leadership, are decisive in shaping instructors’ engagement with AI.
The chapter is particularly effective in avoiding a deficit-based
framing of instructors’ hesitancy. Challenges are situated within
structural and contextual constraints, emphasizing the need for
systemic support rather than placing responsibility solely on
individuals. This approach contributes important nuance to debates on
educational innovation and underscores the interconnectedness of
institutional policy, pedagogy, and technology.
Chapter 6: Tools for AI Literacy Development and Enhancing
Instructors’ Methodological Repertoire
Chapter 6 represents the most practice-oriented section of the book,
offering a rich repertoire of methods, activities, and tools for
developing AI literacy. Drawing on research findings and teaching
experience, the chapter addresses background knowledge development,
confidence building, prompt engineering, ethical awareness,
self-testing, collaborative learning, and career-oriented
applications.
The discussion of prompting and prompt engineering is particularly
well developed, linking language competence, critical thinking, and
digital literacy. The inclusion of concrete frameworks, such as Nazari
and Saadi’s prompting formula, enhances usability for instructors
across disciplines. Equally valuable is the sustained attention to
ethical issues, including hallucinations, misinformation, and bias.
Rather than treating ethics as an abstract concern, the author
proposes hands-on classroom activities that encourage students to
critically interrogate AI outputs.
The extensive collection of tools and resources further strengthens
the chapter’s practical contribution. Examples range from AI text
generators and paraphrasing tools to visualization software and
collaborative platforms. While the density of examples may be
challenging for some readers, the structured approach demonstrates the
potential of AI for enhancing both pedagogical practice and learners’
skill development.
Chapter 7: Conclusion and Future Directions
The final chapter synthesizes theoretical, empirical, and practical
strands of the book while outlining a forward-looking research agenda.
The author reiterates that sustainable AI integration depends on the
development of resilient, reflective AI literacy among both learners
and instructors.
Future research directions are thoughtfully articulated. The author
calls for longitudinal studies, personalized training pathways, and
investigations into emerging challenges, such as deepfakes and
multimodal AI literacy. The discussion links AI literacy to
employability and institutional policy, underscoring the broader
societal relevance of the book’s contributions. Emphasis is placed on
experiential learning, collaborative projects, and real-world
application of AI skills to ensure that literacy is not only
conceptual but also practical and transferable.
EVALUATION
Overall, this monograph makes a substantial contribution to the
literature on AI in higher education. Its strengths lie in its
integrative approach, empirical grounding, and sustained attention to
pedagogy and ethics. The dual focus on learners and instructors is
particularly valuable, as is the operationalization of AI literacy
through the AI-TPACK framework.
While the reliance on self-reported data represents a minor
limitation, the author explicitly acknowledges this and situates
findings within their methodological context. The book succeeds in
offering both conceptual clarity and actionable guidance. It will
appeal to researchers in educational technology, teacher educators,
higher education practitioners, and policy-makers seeking informed
approaches to AI integration.
By combining theory, empirical evidence, and pedagogical practice, the
book provides a nuanced and timely exploration of AI literacy in
higher education. It avoids both utopian and dystopian narratives,
advocating instead for reflective, ethically grounded, and
pedagogically informed engagement with AI. As such, it is an essential
resource for institutions and individuals navigating the rapidly
evolving landscape of AI-enhanced education. Its comprehensive scope,
practical tools, and forward-looking perspective make it a benchmark
for future research and practice in this critical area.
REFERENCES
European Union. (2024). Regulation (EU) 2024/1689 on artificial
intelligence (AI Act). https://eur-lex.europa.eu/eli/reg/2024/1689/oj
Fekete, I. (2025). Artificial Intelligence Literacy in Higher
Education: Theory and Practice from a European Perspective.
Multilingual Matters.
Vygotsky, L. S. (1978). Mind in society: The development of higher
psychological processes. Harvard University Press.
ABOUT THE REVIEWER
Wen Zhang is a machine learning data analyst and NLP specialist with
extensive experience in generative AI, large language models, prompt
engineering, and multilingual and multimodal data optimization. She
also has substantial college-level teaching experience in Chinese and
Japanese, including curriculum development and classroom instruction.
Holding M.A. degrees in Computational Linguistics, Linguistics, and
Language Education, she combines hands-on expertise with AI
technologies and deep knowledge of applied linguistics and language
pedagogy. Her professional background bridges AI, language education,
and multilingual computational analysis, providing a strong foundation
for engaging with research on AI literacy in higher education.



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