Special Issue Announcements

The Educational Point invites proposals for special issues to appear in 2027 and 2028. Proposal submissions should be sent to editor@educationalpoint.net.

New Special Issue Announcement

Title: Artificial Intelligence and the Future of Higher Education: Learner Agency, Autonomy and the Transformation of Teaching and Learning

Abstract submission deadline: December 10, 2026

Guest Editor: Dr. Abílio Afonso Lourenço, University of Minho, Portugal. ORCID: https://orcid.org/0000-0001-6920-0412, Email: privadoxy@gmail.com

Information for This Special Issue

Dear Colleagues,

The growing integration of Artificial Intelligence (AI) and, in particular, Generative Artificial Intelligence (GenAI) into Higher Education is profoundly transforming the ways in which knowledge is produced, accessed, interpreted and used in teaching and learning processes. Tools capable of generating texts, analysing information, solving problems, providing feedback and supporting different academic tasks are changing the practices of students and teachers, as well as traditional conceptions of learning, autonomy, assessment and academic responsibility.

However, the central question is not merely to determine how these technologies can make learning faster, more efficient or more personalised. It is important to understand under what conditions their use effectively contributes to developing students' ability to think critically, make informed decisions, regulate their own learning processes and take responsibility for their academic journey. This Special Issue, entitled “Artificial Intelligence and the Future of Higher Education: Learner Agency, Autonomy and the Transformation of Teaching and Learning”, aims to explore the implications of integrating AI into Higher Education, paying particular attention to learner agency, autonomy and self-regulated learning, without overlooking the role of teachers, academic guidance and institutions in creating pedagogically meaningful learning environments.

The proposal is based on a guiding question: How can we ensure that the growing capacity of Artificial Intelligence to perform tasks effectively contributes to developing, rather than replacing, students' ability to think, make decisions and learn autonomously? This question constitutes the conceptual core of the Special Issue and makes it possible to bring together the technological, cognitive, pedagogical, relational and institutional dimensions of the transformation of Higher Education.

General Focus

The central objective of this Special Issue is to critically analyse how Artificial Intelligence is redefining teaching and learning processes in Higher Education and to identify the conditions that make it possible to integrate these technologies in ways that promote human agency, intellectual autonomy and self-regulated learning. The aim is to move beyond approaches focused exclusively on the use of technological tools or the efficiency of AI applications, giving priority to contributions that examine the relationships between technology, cognitive processes, pedagogical decisions and the development of students' competencies.

A particularly relevant aspect is the need to distinguish between improvements in academic performance resulting from the use of AI and the effective development of students' competencies and knowledge. The ability of an AI system to produce answers, solve problems or perform tasks does not necessarily mean that students have developed the cognitive processes required to carry out these activities autonomously. In this respect, it is important to investigate the conditions under which AI support promotes learning, intellectual engagement and the progressive development of autonomy, and the circumstances in which it may encourage excessive delegation of cognitive processes and compromise these same aims.

Particular attention will be paid to the interaction between three fundamental dimensions:

Artificial Intelligence and the Transformation of Higher Education: The evolution of AI technologies and their implications for institutions, curricula, academic practices and teaching and learning models.
Learner Agency and Autonomy: The active role of students in setting goals, planning, monitoring and evaluating their own learning, including their ability to use AI resources critically without relinquishing intellectual control or responsibility for the decisions they make.
Transformation of Teaching and Learning: The renewal of pedagogical approaches, teaching methodologies, assessment processes and academic guidance practices, taking into account the new opportunities and challenges arising from the interaction between students, teachers and AI systems.

The Special Issue welcomes contributions from different scientific fields and Higher Education contexts, including studies involving students, teachers, researchers, academic leaders and higher education institutions. Empirical studies, theoretical and conceptual research, systematic and integrative literature reviews, case studies and comparative analyses that contribute to a deeper understanding of the transformation processes currently underway are particularly welcome.

Relationship with the Existing Literature

The literature on Artificial Intelligence in education has grown significantly, accompanying the expansion of AI applications and, more recently, Generative AI tools. A substantial part of this research has focused on the use of specific tools, learning personalisation, task automation, content production, automated feedback and the possibilities for pedagogical innovation. Although these approaches are important, there remains a need to deepen our understanding of the implications of AI for students' intellectual development and self-regulation processes. In particular, it is important to understand how the use of these technologies influences the ability to formulate problems, evaluate information, exercise critical judgement, make decisions and take responsibility for one's own learning.

This Special Issue seeks to contribute to this debate by bringing together research on AI and theoretical and empirical contributions relating to learner agency, autonomy, self-regulated learning, metacognition, motivation and academic responsibility. A distinctive element of this proposal is its intention to examine not only what AI makes possible, but also what students still need to learn and develop when they have access to systems capable of performing increasingly complex cognitive tasks. Within this framework, the aim is to explore the conditions under which AI can function as a learning support resource without leading to the indiscriminate delegation of cognitive processes, technological dependence, or a reduction in students' intellectual engagement. The Special Issue therefore seeks to promote an integrated approach that simultaneously considers the potential of AI, the limitations of its use and the need to preserve the centrality of human agency in educational processes.

Contribution to Sustainable Development

The responsible integration of Artificial Intelligence into Higher Education can contribute to different dimensions of sustainable development, particularly by expanding learning opportunities, promoting inclusion and improving access to knowledge. This Special Issue establishes a direct relationship with Sustainable Development Goal 4 (SDG 4 — Quality Education) by exploring how AI can support inclusive, equitable and quality learning experiences, while simultaneously promoting autonomy, critical thinking and the capacity for lifelong learning.

The proposal also considers its links with SDG 9 (Industry, Innovation and Infrastructure), through the analysis of technological innovation and the digital transformation of Higher Education institutions, and with SDG 10 (Reduced Inequalities), particularly with regard to accessibility, digital inclusion and differences in opportunities to use AI and develop related competencies. Attention will be paid to the conditions required to ensure that the integration of these technologies does not deepen existing inequalities, but instead contributes to more accessible, responsible and sustainable educational environments.

Topics

The Special Issue welcomes papers addressing, among others, the following thematic areas:

1. Artificial Intelligence and Generative AI in Higher Education

Applications of Artificial Intelligence and Generative AI in higher education.
The evolution of AI-supported teaching and learning environments.
Opportunities and limitations of generative AI in academic contexts.
AI-supported knowledge construction and academic work.
Institutional strategies for AI integration in higher education.
Emerging models of human–AI interaction in academic environments.

2. Learner Agency, Autonomy and Self-Regulated Learning

Learner agency and student autonomy in AI-supported learning.
Self-regulated learning in the age of Artificial Intelligence.
Metacognition, planning, monitoring and reflection in AI-supported learning.
Student motivation, self-efficacy and autonomous learning.
Distributed and delegated regulation in human–AI learning environments.
The development of independent learning and lifelong learning competencies.

3. AI Literacy, Critical Thinking and Human Judgement

AI literacy and critical understanding of intelligent systems.
Critical thinking and evaluation of AI-generated information.
Human judgement, epistemic responsibility and decision-making.
Information verification, credibility and the assessment of AI outputs.
Cognitive and metacognitive competencies in AI-mediated learning.
The development of responsible and reflective AI use.

4. Personalised Learning and AI-Supported Educational Practices

AI-supported personalised learning pathways.
Adaptive learning environments and intelligent tutoring systems.
Learning analytics and data-informed educational decisions.
Intelligent feedback and formative assessment.
Personalisation, student agency and learner autonomy.
Opportunities and limitations of adaptive educational technologies.

5. Cognitive Offloading, Technological Dependence and Academic Responsibility

Cognitive offloading and the delegation of academic tasks to AI.
Technological dependence and its implications for learning.
Cognitive engagement and intellectual effort in AI-supported learning.
Academic integrity, authorship and responsibility.
The balance between technological assistance and independent thinking.
Risks of automation and the preservation of human cognitive capacities.

6. Teaching Innovation, Assessment and Pedagogical Transformation

Innovative pedagogical approaches in AI-supported higher education.
Curriculum redesign and the integration of AI literacy.
Assessment practices in the age of Generative AI.
Authentic assessment, process-oriented evaluation and academic responsibility.
AI-supported formative feedback and learning assessment.
Active learning, problem-based learning and collaborative approaches.
Faculty development and professional learning for AI-integrated teaching.

7. Student–Teacher–AI Interaction and the Future of Academic Guidance

Student–teacher–AI interaction in higher education.
The evolving role of teachers in AI-supported learning environments.
Academic guidance, counselling and mentoring in the age of AI.
Human support, feedback and the development of learner autonomy.
Teacher agency and professional judgement in AI-mediated education.
Relational and collaborative dimensions of learning with AI.

8. Ethical, Social and Institutional Challenges of AI Integration

Ethical principles for the integration of AI in higher education.
Privacy, data protection and algorithmic transparency.
Bias, fairness and accessibility in AI-supported education.
Institutional policies and governance of AI use.
Digital inequalities and the responsible adoption of AI.
Academic freedom, human oversight and institutional accountability.
Sustainable and inclusive models of AI integration.

Conclusion

Artificial Intelligence is profoundly changing the conditions under which knowledge is produced, accessed and used in Higher Education. However, its educational value should not be assessed solely in terms of its ability to automate tasks, accelerate procedures or improve students' immediate performance. A more fundamental question is whether, and under what conditions, the use of AI contributes to developing students' ability to think critically, make informed decisions, regulate their own learning and take responsibility for their academic and intellectual development. This Special Issue aims to bring together scientific contributions that make it possible to understand and guide this transformation, combining the technological potential of AI with the centrality of human agency, autonomy and academic responsibility. Researchers from different scientific fields are invited to submit papers that go beyond the description of isolated technological applications and contribute to the development of conceptual perspectives, pedagogical models and empirical evidence concerning the future of Higher Education.

The ultimate objective is to promote a scientifically grounded reflection on an essential question: how can Artificial Intelligence be transformed into an effective resource for developing more autonomous, critical students who are capable of continuing to learn, without replacing the cognitive processes and responsibilities that make learning genuinely human?

Manuscript Submission

Abstract should be submitted using the Educational Point's online submission system. An abstract can be submitted using the journal’s submission system, including a title page but without the full text. Inquiries about abstract submission and questions should be directed to privadoxy@gmail.com & editor@educationalpoint.net.

In addition, the authors can submit their full paper through the system. While submitting an abstract or full paper, the authors must choose the "issue type" as "special issue" on the submission system. Manuscripts must be submitted online through Educational Point's manuscript submission system. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are subject to peer review. Accepted articles will be published continuously in the journal (as soon as accepted) and listed together on the special issue website.

Submitted manuscripts should not have been published previously, nor should they be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are rigorously evaluated through a double-blind peer-review process. The guide for authors and other relevant submission information is available on the Instructions for Authors page.

Keywords

Artificial Intelligence; Generative AI; Higher Education; Learner Agency; Student Autonomy; Self-Regulated Learning; AI Literacy; Critical Thinking; Personalised Learning; Academic Responsibility; Pedagogical Transformation; Human–AI Interaction.

Sustainable Development Goals

SDG 4 — Quality Education
SDG 9 — Industry, Innovation and Infrastructure
SDG 10 — Reduced Inequalities

Benefits of Publishing in a Special Issue

Enhanced thematic visibility: Grouping articles around a common scientific issue makes it easier for researchers to identify and access relevant research.
Greater research dissemination: Bringing together contributions on AI and Higher Education can increase the visibility of research published in this area.
Research network expansion: The Special Issue may bring together researchers from different scientific fields and institutional contexts, promoting new opportunities for collaboration.
Academic debate and knowledge exchange: The diversity of theoretical and methodological perspectives may contribute to deepening the debate on the relationship between AI, autonomy and pedagogical transformation.
Potential for future publication: If this is consistent with the journal's editorial practices, the subsequent publication of a selection of papers in book or thematic volume format may be considered.

Proposed Schedule

The following schedule constitutes an initial proposal, flexible and adjustable according to the journal's availability and editorial procedures:

Abstract submission deadline: December 10, 2026
Full paper submission deadline: March 1, 2027
Peer-review and revision period: May 30, 2027
Publication: June 1, 2027

Publication may take place continuously as articles are accepted, with the papers subsequently brought together on the Special Issue webpage.