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Algorithms
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Artificial Intelligence in Education: Innovations and Implications
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Artificial Intelligence in Education: Innovations and Implications

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 4599

Special Issue Editors

Dr. Tingting Liu

E-Mail Website
Guest Editor
College of Education, Hubei University, Wuhan 430062, China
Interests: intelligent education; deep learning; pattern recognition; learning behavior analysis; K-12 education; large language model
Special Issues, Collections and Topics in MDPI journals
Dr. Hai Liu

E-Mail Website
Guest Editor
Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China
Interests: educational technology; human-computer interaction; head pose estimation; facial expression recognition; gaze estimation; human pose estimation; large language model
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to the Special Issue on ‘Artificial Intelligence in Education: Innovations and Implications’ for the Algorithms journal. This Special Issue aims to explore the rapid advancements and transformative potential of artificial intelligence in the educational landscape. The details can be found below.

Artificial intelligence has dramatically reshaped various sectors and education is no exception. From personalized learning systems and adaptive assessments to intelligent tutoring and automated content generation, artificial intelligence is driving significant changes in how education is delivered. Recent advancements in machine learning, natural language processing and computer vision have enabled the creation of more intuitive educational tools that dynamically respond to individual learners' needs. As education evolves, it increasingly requires novel AI algorithms to address these changing needs. Artificial intelligence’s integration into education bridges the fields of artificial intelligence and educational technology and algorithms such as deep learning, image recognition and predictive analytics are central to the development of smarter, more adaptive learning systems. For example, understanding students' emotional states requires advanced algorithms for facial expression recognition, which can help gauge engagement and emotional responses. Artificial intelligence-powered vision algorithms can estimate students' head pose and gaze direction, offering valuable insights into attention levels during lessons. By analyzing this data, educators can identify whether students are focused or distracted, enabling them to make timely adjustments to improve engagement. Furthermore, artificial intelligence-driven knowledge graphs can model complex relationships between concepts, making learning materials more intuitive and personalized.

This Special Issue provides a platform for researchers to present their theoretical and technological studies on emerging artificial intelligence algorithms in education, particularly those related to adaptive learning, predictive analytics and real-time data processing. We invite contributions that explore how artificial intelligence can transform educational practices, from developing intelligent tutoring systems to creating dynamic learning environments. These advances have the potential to revolutionize educational technologies, making them more interactive, adaptive, and effective in meeting diverse learning goals.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Large Language model-based algorithms for teaching/learning behavior analysis
  • Face, gesture and body analysis algorithms in the classroom/online learning platform
  • Vision-based head pose estimation and facial expression recognition algorithms
  • Vision-based gaze estimation and human pose estimation algorithms
  • AI-driven automated assessment and feedback technology
  • Education knowledge graph construction and intelligent reasoning algorithms
  • Scalable application of artificial intelligence in online education platforms
  • Artificial intelligence-supported educational equity and inclusive education practices
  • Artificial intelligence-powered intelligent tutoring system algorithms
  • Algorithm-based intelligent agent learning environments

I look forward to receiving your contributions.

Dr. Tingting Liu
Dr. Hai Liu
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 single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms 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 1800 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

  • large language model-based algorithms for teaching/learning behavior analysis
  • face, gesture and body analysis algorithms in the classroom/online learning platform
  • vision-based head pose estimation and facial expression recognition algorithms
  • vision-based gaze estimation and human pose estimation algorithms
  • AI-driven automated assessment and feedback technology
  • education knowledge graph construction and intelligent reasoning algorithms
  • scalable application of artificial intelligence in online education platforms
  • artificial intelligence-supported educational equity and inclusive education practices
  • artificial intelligence-powered intelligent tutoring system algorithms
  • algorithm-based intelligent agent learning environments

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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30 pages, 2403 KB  
Article
Gamification in Education and Its Impact on Student Academic Performance: A Conceptual Model Based on Systematic Literature Review and PLS-SEM Analysis
by Ahmad Almufarreh
Algorithms 2026, 19(2), 143; https://doi.org/10.3390/a19020143 - 10 Feb 2026
Cited by 1 | Viewed by 2758
Abstract
Enhancing student academic performance remains a critical challenge for educators and administrators. Among various interventions, gamification has gained increasing attention as a promising approach to improving learning outcomes. However, existing research on the role of gamification in education remains fragmented due to its [...] Read more.
Enhancing student academic performance remains a critical challenge for educators and administrators. Among various interventions, gamification has gained increasing attention as a promising approach to improving learning outcomes. However, existing research on the role of gamification in education remains fragmented due to its multidisciplinary nature. This study aims to synthesize current knowledge through a systematic review in order to develop and validate a conceptual framework linking gamification to student academic performance. By following the PRISMA framework, 62 relevant studies were reviewed, highlighting several recurring themes, including game design and development, student performance outcomes, and critical aspects of gamification such as cognitive development, motivation, and empowerment. Key mechanisms identified include active learning, personalized and adaptive learning, and collaborative interaction. Reported outcomes of gamification interventions include higher test scores, reduced anxiety and stress, and increased engagement and positive attitudes toward learning. Building on these findings, the resulting conceptual framework was validated through empirical research. Data were collected from 289 students from Saudi Arabia using a structured survey instrument, and Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for analysis. The results provide empirical support for the proposed framework, confirming gamification as a significant driver of improved student academic performance. The findings provide practical implications for educators and policymakers seeking to leverage gamification as a strategic tool for enhancing student learning outcomes. Full article
(This article belongs to the Special Issue Artificial Intelligence in Education: Innovations and Implications)
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25 pages, 920 KB  
Systematic Review
A Systematic Literature Review on the Pedagogical Implications and Impact of GenAI on Students’ Critical Thinking
by Trini Balart, Brayan Díaz and Kristi Shryock
Algorithms 2026, 19(3), 179; https://doi.org/10.3390/a19030179 - 27 Feb 2026
Viewed by 1370
Abstract
Critical Thinking (CT) is recognized as a foundational competency for professional readiness, innovation, and ethical reasoning in higher education, enabling students to analyze information, evaluate evidence, and make reasoned decisions in complex environments. The rapid integration of Generative Artificial Intelligence (GenAI) tools, such [...] Read more.
Critical Thinking (CT) is recognized as a foundational competency for professional readiness, innovation, and ethical reasoning in higher education, enabling students to analyze information, evaluate evidence, and make reasoned decisions in complex environments. The rapid integration of Generative Artificial Intelligence (GenAI) tools, such as large language models, presents new opportunities and risks for CT development. This study conducts a systematic literature review to synthesize empirical evidence on the pedagogical implications and cognitive impact of GenAI on students’ CT. Following PRISMA guidelines, and search terms around GenAI Tools, Critical Thinking And Higher Education, on five major education research databases—Web of Science; Scopus; EBSCOhost (Education Source, ERIC, and APA PsycInfo); and Compendex and Inspec (Elsevier)—63 empirical studies published between January 2023 and April 2025 were analyzed across higher education contexts, disciplines, and intervention designs. Results indicate that GenAI offers notable cognitive affordances, including scaffolding reflective reasoning, promoting self-regulation, and facilitating iterative dialogue and argument evaluation. Pedagogical strategies clustered into four primary integration typologies: AI-based feedback prompts, dialogue simulation and reflection, AI-supported peer review, and critical engagement with AI-generated content. Nearly half of the studies reported statistically significant CT improvements, particularly when GenAI use was guided by structured prompts, reflective activities, and performance-based assessment. However, multiple risks persist, including cognitive offloading, uncritical acceptance of AI outputs, and diminished intellectual autonomy, especially in unguided or surface-level usage. This review highlights the need for intentional pedagogical design, validated CT assessment tools, and longitudinal studies to ensure GenAI acts as a catalyst rather than a substitute for human reasoning. By identifying effective integration strategies and outlining potential pitfalls, this study provides evidence-informed guidance for educators and institutions aiming to responsibly leverage GenAI to strengthen students’ CT skills. Full article
(This article belongs to the Special Issue Artificial Intelligence in Education: Innovations and Implications)
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