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Education Sciences
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AI in Education: Transforming Curriculum, Pedagogy, and Assessment
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AI in Education: Transforming Curriculum, Pedagogy, and Assessment

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 4911

Editors

Dr. Anna Mavroudi

E-Mail Website
Guest Editor
Department of Education, University of Oslo, 0371 Oslo, Norway
Interests: technology-enhanced learning; higher education; faculty professional development; university pedagogy; pandemic pedagogies; learning analytics; distance education; personalized learning
Prof. Dr. Kyparisia Papanikolaou

E-Mail Website
Guest Editor
Department of Education, School of Pedagogical & Technological Education, 141 21 Athens, Greece
Interests: adaptive & intelligent learning environments; learning design; teacher professional development focusing on technology enhanced learning
Prof. Dr. Charoula Angeli

E-Mail Website
Guest Editor
Department of Education, University of Cyprus, 1678 Nicosia, Cyprus
Interests: AI in education; computational thinking; educational robotics; technological pedagogical content knowledge
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Historically, Artificial Intelligence (AI) has been used in education for nearly six decades, performing various tasks like personalized learning and student assessments. However, the capabilities of Generative AI (GenAI) introduce new opportunities and challenges for the same tasks. For example, it can create enhanced personalized learning experiences, but at the same time, it calls for a shift in student assessment methods to prevent cheating and plagiarism. Since GenAI is still in its early stages, it is also important to explore whether and how it can offer new insights into curriculum reform and pedagogical innovation with a focus on teaching and learning processes.

This Special Issue aims to further investigate learning and teaching with AI across all education levels and in various contexts, including teacher professional development. In particular, we welcome papers contribute to understanding AI’s impact on curriculum and pedagogy innovation. While the scope of the Special Issue encompasses all forms of AI in education, the importance of exploring GenAI cannot be overlooked.

Suggested themes include but are not limited to the following:

  • Student engagement with AI, such as behavioral, emotional, (meta)cognitive engagement
  • AI as a cognitive partner
  • Teacher professional development for AI in education
  • Student partnerships with AI
  • AI in relation to contemporary cultures of text in educational settings
  • AI literacy among students and/or teachers
  • New perspectives on learning theory, on pedagogical processes, or on curriculum design supported by AI
  • Ethical aspects of using AI for teaching and learning purposes

Dr. Anna Mavroudi
Prof. Dr. Kyparisia Papanikolaou
Prof. Dr. Charoula Angeli
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Education Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • pedagogical innovation
  • curriculum reform
  • teacher pedagogical development
  • large language models

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Published Papers (3 papers)

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27 pages, 1878 KB  
Article
Do AI Grading Systems Systematically Differ from Human Teachers’ Grading? Evidence of Bias and Consistency in Educational Assessment
by Konstantinos Papageorgiou and Christos Pierrakeas
Educ. Sci. 2026, 16(7), 1147; https://doi.org/10.3390/educsci16071147 - 17 Jul 2026
Viewed by 798
Abstract
The rapid development of artificial intelligence (AI) has introduced new possibilities for transforming educational assessment processes. Among these developments, AI-assisted grading systems have attracted increasing attention due to their potential to improve efficiency, consistency, and scalability of student evaluation. The present study examines [...] Read more.
The rapid development of artificial intelligence (AI) has introduced new possibilities for transforming educational assessment processes. Among these developments, AI-assisted grading systems have attracted increasing attention due to their potential to improve efficiency, consistency, and scalability of student evaluation. The present study examines the role of artificial intelligence in student grading by comparing AI-generated scores with human teacher evaluations and by exploring teachers’ perceptions regarding the use of AI in educational assessment. The research adopts a quantitative comparative design. Student-written responses were independently evaluated by teachers and AI systems, and the resulting scores were statistically analyzed to examine the level of agreement between the two grading approaches. In addition, a structured questionnaire was administered to teachers to investigate their attitudes toward AI-assisted grading. The findings indicate that while some AI systems produce scores comparable to human evaluators, others exhibit statistically significant differences, highlighting variability across models. Furthermore, AI systems were found to produce more consistent grading outcomes in relation to the corresponding human evaluators. Nevertheless, teachers recognized the potential of AI to reduce the time required for assessment tasks. However, concerns related to fairness, transparency, and the interpretation of complex student responses remain important considerations. Overall, the results suggest that artificial intelligence can effectively support educational assessment when implemented within hybrid evaluation models that combine automated analysis with human pedagogical oversight. Full article
(This article belongs to the Special Issue AI in Education: Transforming Curriculum, Pedagogy, and Assessment)
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23 pages, 722 KB  
Article
Enhancing Statistical Thinking in Higher Education Through Pedagogically Designed Use of Interactive Whiteboards
by Roman Yavich
Educ. Sci. 2026, 16(4), 636; https://doi.org/10.3390/educsci16040636 - 16 Apr 2026
Viewed by 500
Abstract
Although interactive technologies such as interactive whiteboards are increasingly used in higher education, empirical evidence regarding their pedagogical role in statistics education remains limited. Existing studies often focus on technology adoption rather than instructional design. This study examines the effectiveness of interactive whiteboards [...] Read more.
Although interactive technologies such as interactive whiteboards are increasingly used in higher education, empirical evidence regarding their pedagogical role in statistics education remains limited. Existing studies often focus on technology adoption rather than instructional design. This study examines the effectiveness of interactive whiteboards when embedded within a pedagogically designed instructional framework aimed at supporting statistical thinking. A mixed-methods, quasi-experimental design with pre- and post-test measures (N = 126) was employed to compare learning outcomes and student perceptions in an introductory university statistics course taught either through traditional lectures or through an interactive approach emphasizing dynamic visualization, collective interpretation, and formative feedback. Mediation was tested using bootstrapped indirect effects and complemented by qualitative thematic analysis. Students in the interactive condition demonstrated significantly greater gains in statistical reasoning (Cohen’s d = 0.94, 95% CI [0.57, 1.31]), particularly in tasks involving data interpretation and reasoning about variability. Mediation analysis indicated that two student self-report measures—perceived clarity of instruction and formative feedback quality—together accounted for 63% of the total effect. The interactive format was especially beneficial for students with lower prior knowledge, reducing achievement gaps by 34%. These findings are consistent with the view that interactive technologies support conceptual learning most effectively when embedded in deliberate pedagogical designs promoting visualization, collective reasoning, and real-time feedback, highlighting the central role of instructional design over technological presence. Full article
(This article belongs to the Special Issue AI in Education: Transforming Curriculum, Pedagogy, and Assessment)
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34 pages, 727 KB  
Systematic Review
Extracurricular Activities and Academic Performance: A Systematic Review with a Focus on AI and Machine-Learning Applications in Education
by Aspa Alexaki, Dimitrios Michalopoulos, Dimitris Papadopoulos and Konstantinos C. Giotopoulos
Educ. Sci. 2026, 16(7), 1067; https://doi.org/10.3390/educsci16071067 - 3 Jul 2026
Viewed by 1580
Abstract
Extracurricular activities (ECAs) are widely recognized as contributors to holistic student development, although the nature and magnitude of their associations with academic performance remain context-dependent across educational levels. At the same time, artificial intelligence and machine learning are increasingly used in many educational [...] Read more.
Extracurricular activities (ECAs) are widely recognized as contributors to holistic student development, although the nature and magnitude of their associations with academic performance remain context-dependent across educational levels. At the same time, artificial intelligence and machine learning are increasingly used in many educational settings, while variables that are not directly related to academic content, such as participation in extracurricular activities, are very rarely used. The present review is a systematic literature review conducted according to the PRISMA 2020 framework. It consisted of 30 empirical studies published between 2010 and 2025 which examined the relationship between ECAs and academic performance, with focused attention on studies that incorporated AI and ML techniques. The majority of the studies, across all education levels, reported neutral to positive associations between ECAs and academic performance metrics, such as grades, test scores, and engagement indicators. The evidence suggests that moderate involvement in ECAs generally does not harm academic performance, while excessive involvement can generate time conflicts that undermine study. A substantial proportion of the studies reviewed (12 of 30, or 40%) applied AI or ML methods to predict academic outcomes. These studies reported improvements in predictive accuracy when ECA-related variables were included, though performance metrics varied widely across algorithms, datasets, and outcome measures, precluding direct comparison. Overall, the findings suggest that ECAs complement student development, with effects contingent on activity type, intensity, and educational context. Future research should prioritize longitudinal designs, standardized ECA measurement, and interpretable AI models that support transparent and equitable decision-making. Longitudinal studies are needed to clarify the temporal sequence of associations between ECA participation and academic outcomes, and to assess equity of access. Full article
(This article belongs to the Special Issue AI in Education: Transforming Curriculum, Pedagogy, and Assessment)
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