Higher Education in the Age of AI: Instructional Innovation, Societal Equity, and Infrastructure Reliance

A special issue of Education Sciences (ISSN 2227-7102). This special issue belongs to the section "Higher Education".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 4975

Special Issue Editor


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Guest Editor
Sociology, Paderborn University, 33098 Paderborn, Germany
Interests: higher education research; digital divide; AI-based methods; AI-enhanced pedagogy

Special Issue Information

Dear Colleagues,

Artificial intelligence is rapidly transforming higher education by reshaping teaching, learning, and institutional practices. As universities adopt adaptive systems, intelligent tutoring solutions, and generative tools, new opportunities arise for pedagogical innovation. But risks of social stratification, algorithmic bias, and over-reliance on proprietary technologies also arise. Ensuring that AI promotes access, inclusion, and academic integrity, rather than reproducing existing inequalities or propagating misinformation, requires a comprehensive understanding of the interactions among AI, education, and organizational structures.

In this context, we are pleased to announce a Call for Papers for our Special Issue “Higher Education in the Age of AI: Instructional Innovation, Societal Equity, and Infrastructure Reliance”. We invite authors to critically examine how AI is transforming higher education and its societal impacts; to identify ethical and legal considerations in AI‐mediated pedagogical contexts; to explore both opportunities and pitfalls of AI‐enhanced teaching and learning; and to propose pedagogical strategies for addressing bias, hallucinations, and other technical challenges.

In doing so, we aim to bring together a wide range of perspectives and approaches to better understand the impact of artificial intelligence in Higher Education. Authors are encouraged to address some of the following topics in the context of Higher Education:

  • AI‐supported pedagogical redesign in higher education; 
  • Approaches to algorithmic bias, fairness, and transparency in education; 
  • Societal change, social justice, and the digital divide; 
  • Dependence on proprietary systems and infrastructure risks; 
  • Ethical, political, and governance models for the responsible use of AI in Higher Education.

Prof. Dr. Isabel Steinhardt
Guest Editor

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Keywords

  • AI in higher education
  • pedagogical innovation
  • societal responsibility
  • technological dependency
  • algorithmic bias
  • AI hallucinations
  • ethical governance
  • digital divide

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Published Papers (3 papers)

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Research

17 pages, 2387 KB  
Article
Research on Learning Analytics–Driven AI-Supported Blended Teaching: A Case Study of the Undergraduate Course Combustion Science
by Hongtao Li, Liqiang Liang, Yingyi Han, Chenyang Zhang, Qingsong Song and Zhijie Han
Educ. Sci. 2026, 16(6), 876; https://doi.org/10.3390/educsci16060876 - 2 Jun 2026
Viewed by 154
Abstract
Against the backdrop of the digital transformation in engineering education, this study developed and implemented a Learning Analytics (LA)-driven and Artificial Intelligence (AI)-supported blended learning model to address structural challenges in the “Combustion” course, including highly abstract theories, experimental safety risks, and compressed [...] Read more.
Against the backdrop of the digital transformation in engineering education, this study developed and implemented a Learning Analytics (LA)-driven and Artificial Intelligence (AI)-supported blended learning model to address structural challenges in the “Combustion” course, including highly abstract theories, experimental safety risks, and compressed instructional hours. Moving beyond mere technical stacking, the model establishes a closed-loop data ecosystem that integrates “pre-class adaptive diagnosis, in-class contextualized internalization, and post-class personalized transfer,” while deeply embedding engineering ethics and sustainability issues related to carbon neutrality. A one-semester quasi-experimental study (Experimental N = 60, Control N = 60) was conducted, utilizing a triangulated assessment of final exam scores, platform-based behavioral trajectories, and semi-structured interviews. The results showed that the experimental group achieved significantly higher final assessment scores than the control group (82.4 ± 5.7 vs. 73.2 ± 6.9), with normality tests supporting the use of parametric analysis and Analysis of Covariance (ANCOVA) indicating a significant instructional effect after controlling for Grade Point Average (GPA) and pre-test scores. Furthermore, behavioral analysis confirms that the LA mechanism significantly enhances students’ self-regulated learning and engagement by increasing the visibility of the learning process. This study provides an evidence-based reform paradigm for engineering curricula to achieve the synergistic cultivation of knowledge acquisition, competency development, and value alignment within constrained instructional timeframes. Full article
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27 pages, 1344 KB  
Article
Ethical Challenges of Artificial Intelligence in Higher Education: A Four-Pillar Student-Activity Framework for Institutional Governance
by Radovan Madleňák, Lucia Madleňáková, Viktória Cvacho and Daniel Gachulinec
Educ. Sci. 2026, 16(4), 555; https://doi.org/10.3390/educsci16040555 - 2 Apr 2026
Viewed by 1933
Abstract
This study introduces a four-pillar student-activity framework (Studying and Learning, Research and Projects, Personal and Career Development, and Campus and Community Life) to analyze AI’s ethical challenges in higher education. Drawing on peer-reviewed sources from 2022 to 2025, we identify recurring risks across [...] Read more.
This study introduces a four-pillar student-activity framework (Studying and Learning, Research and Projects, Personal and Career Development, and Campus and Community Life) to analyze AI’s ethical challenges in higher education. Drawing on peer-reviewed sources from 2022 to 2025, we identify recurring risks across pillars: academic integrity, privacy/data protection, bias/fairness/equity, student agency/(de)skilling, and governance gaps. We distill three cross-pillar principles: disclosure plus process evidence (e.g., prompt/version logs), privacy-by-design, and proportionality and equity/fairness scaffolds (institutional access, bias audits, and multilingual support). These translate into actionable strategies for assessment redesign, research supervision, career services, and campus operations. The framework unifies fragmented discourse, supports institutional decision making, and reveals gaps for longitudinal and causal research. It demonstrates that responsible AI use emerges when processes are visible, data practices are proportionate, and access is equitable, amplifying human learning without eroding trust or integrity. Full article
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21 pages, 398 KB  
Article
Infusing Gen Z’s Pro-Ecological Intentions: From AI Hallucinations to the Ethical Governance of Green Digital Footprints
by Mostafa Aboulnour Salem
Educ. Sci. 2026, 16(3), 431; https://doi.org/10.3390/educsci16030431 - 12 Mar 2026
Cited by 2 | Viewed by 764
Abstract
Green AI contributes to digital sustainability in higher education by encouraging computationally efficient technologies and responsible digital practices. Despite growing interest in sustainable AI, empirical evidence remains limited on how Gen Z students develop socially responsible intentions toward the use of sustainability-aligned AI, [...] Read more.
Green AI contributes to digital sustainability in higher education by encouraging computationally efficient technologies and responsible digital practices. Despite growing interest in sustainable AI, empirical evidence remains limited on how Gen Z students develop socially responsible intentions toward the use of sustainability-aligned AI, particularly within a single host-country higher-education context. This study examines these intentions among students enrolled in Saudi Arabia, using a culturally diverse sample of Saudi and international students while treating national origin as a demographic characteristic rather than a basis for cross-national comparison. The research also addresses emerging concerns related to AI hallucinations and ethical governance in educational settings. An integrated framework is employed that combines the instrumental appraisal logic of UTAUT with responsibility-oriented constructs. The model includes Sustainable Performance Value (SPV), Responsible Use Ease (RUE), Ethical Social Norms (ESN), Institutional Ethical Support (IES), Responsible AI Competence (RAC), AI Hallucination Awareness (AHA), and Green Digital Responsibility (GDR) as predictors of Socially Responsible Intentions (SRI). Data were collected through an anonymous survey of 1159 higher-education students residing and studying within the Saudi higher-education system. The study design reflects one institutional context rather than a multi-country comparison. The findings show strong explanatory and predictive capability (R2 = 0.64; Q2 = 0.43). SPV, RAC, AHA, and GDR are the strongest predictors of SRI, while RUE shows a moderate association and IES provides contextual support; ESN is not significant. The results highlight the importance of values, competence, and risk awareness in shaping the responsible use of AI. Implications focus on governance and curriculum strategies that support sustainability-aligned engagement with AI in higher education. Full article
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