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Human–Computer Interactions and Computer-Assisted Education
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Human–Computer Interactions and Computer-Assisted Education

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 20 November 2026 | Viewed by 4171

Special Issue Editors

Dr. Chien-Hung Lai

E-Mail Website
Guest Editor
Department of Information and Computer Engineering, Chung Yuan Christian University, Taoyuan City 320314, Taiwan
Interests: learning performance diagnosis; game-based learning; analysis of the associative reasoning learning method; use of handheld devices and computer systems to assist learning; assisted programming learning strategies
Prof. Dr. Gholamreza Anbarjafari

E-Mail Website
Guest Editor
The Intelligent Computer Vision (iCV) Research Lab in the Institute of Technology, University of Tartu, 50411 Tartu, Estonia
Interests: machine learning; computer vision; human–computer interaction; emotion recognition; deep learning; human behaviour analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Rapid advances in human–computer interaction (HCI) and computer-assisted education (CAE) technologies are redefining the dynamics between learners, educators, and digital platforms. This Special Issue seeks to bring together interdisciplinary perspectives on the design, implementation, and evaluation of HCI and CAE systems, with a particular focus on their potential to enhance the educational experience. Key areas of interest include user-centered design principles, adaptive learning methodologies, the integration of artificial intelligence (AI) in education, and innovative strategies for improving accessibility and engagement across diverse learner populations.

In light of the growing prominence and capabilities of generative AI (Gen-AI) technologies, such as large language models, this Special Issue particularly welcomes contributions that examine their transformative potential in educational contexts. Gen-AI holds unprecedented promise for personalizing learning trajectories, automating feedback, and fostering creativity among students. Researchers are invited to explore the ethical considerations, practical challenges, and technical implications of embedding generative AI within HCI and CAE systems. Submissions that propose novel frameworks, methodologies, or applications to address these challenges are especially encouraged.

Dr. Chien-Hung Lai
Prof. Dr. Gholamreza Anbarjafari
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. Information 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

  • human–computer interactions (HCIs)
  • computer-assisted education (CAE)
  • generative AI in education
  • adaptive learning technologies
  • user-centered design

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Published Papers (6 papers)

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Research

22 pages, 3691 KB  
Article
Interpreting Interaction Patterns and Cognitive Strategies in LLM-Supported Exploratory Learning: A Mixed-Methods Analysis Using the DOK Framework
by Yiming Taclis Luo, Ting Liu, Patrick Pang, Dana McKay, Shanton Chang and George Buchanan
Information 2026, 17(3), 288; https://doi.org/10.3390/info17030288 - 14 Mar 2026
Viewed by 242
Abstract
As exploratory learning (EL) is increasingly observed with the use of large language models (LLMs), students demonstrate notably varied levels of engagement and effectiveness when they interact with such LLM-supported learning environments. However, the underlying mechanisms driving these disparities, particularly in how students [...] Read more.
As exploratory learning (EL) is increasingly observed with the use of large language models (LLMs), students demonstrate notably varied levels of engagement and effectiveness when they interact with such LLM-supported learning environments. However, the underlying mechanisms driving these disparities, particularly in how students interact with LLMs, remain underexplored. To address this gap, this observational comparative study systematically investigates the EL strategies of 46 students in two different regions of Asia, classifying 25 distinct strategies across cognitive stages using the Depth of Knowledge model. Our analysis compares strategy usage between high and low-performing student subgroups. The findings reveal: (1) A declining trend in the utilization of EL strategies across ascending cognitive stages. (2) High AWP students employed EL strategies more frequently than their peers, with ten EL strategies exhibiting significant between-group differences. (3) Among students with different AI experience, only a few EL strategies usage and cognitive stages showed significant differences. These insights can help educators and LLM interface designers develop targeted exploratory learning assistance for different types of students and help them build high-level metacognitive processes for effective human–computer interaction. Full article
(This article belongs to the Special Issue Human–Computer Interactions and Computer-Assisted Education)
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22 pages, 6614 KB  
Article
AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs
by Athanasios Angeioplastis, Markos Konstantakis, John Aliprantis, Konstantinos Ordoumpozanis, Dimitrios Varsamis and Alkiviadis Tsimpiris
Information 2026, 17(2), 216; https://doi.org/10.3390/info17020216 - 19 Feb 2026
Viewed by 422
Abstract
Learning Management Systems (LMSs) remain largely static and administrative, often failing to support personalization and inclusive access to learning resources. This paper presents AI for All, a practical approach to building an adaptive, accessible, and inclusive learning experience within a mainstream LMS, [...] Read more.
Learning Management Systems (LMSs) remain largely static and administrative, often failing to support personalization and inclusive access to learning resources. This paper presents AI for All, a practical approach to building an adaptive, accessible, and inclusive learning experience within a mainstream LMS, demonstrated through the PREPARE project (Personalized Education Framework for AI-Enabled Adaptive and AR-Enhanced Learning) implemented in Moodle. PREPARE operationalizes an end-to-end generative AI pipeline that transforms a single authoritative PDF textbook into multimodal learning assets, including chapter summaries, structured notes and slide decks, formative quiz items, video mini-lectures with captions, podcast-style audio, and chapter-level augmented reality (AR) activities. In parallel, the system maintains a hybrid learner model by combining an initial FSLSM/ILS questionnaire with continuous behavior-based profiling derived from Moodle logs. Learner profiles drive non-prescriptive personalization through resource prioritization and recommendations, while preserving learner agency and access to all modalities. We describe the system architecture, Moodle integration mechanisms, and adaptation logic, and report an ongoing mixed-methods evaluation focusing on engagement, interaction diversity, perceived usefulness, and accessibility benefits. The system-level validation and deployment readiness suggest that AI-augmented LMS workflows can reduce instructor authoring effort while improving flexibility and inclusivity, provided that human-in-the-loop validation and privacy-aware analytics are embedded from the outset. Full article
(This article belongs to the Special Issue Human–Computer Interactions and Computer-Assisted Education)
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44 pages, 5926 KB  
Article
User Experience and Usability Evaluation of an Educational Mobile Application Developed for Fostering Ethics Literacy
by Andriani Piki, Nicos Kasenides and Nearchos Paspallis
Information 2026, 17(2), 193; https://doi.org/10.3390/info17020193 - 13 Feb 2026
Viewed by 768
Abstract
The world is constantly challenged by complex crises—from the COVID-19 pandemic and geopolitical tensions to economic uncertainty and severe environmental disasters. During these critical times, individuals need to reflect on ethical values and demonstrate responsible decision-making, integrity, and preparedness to mitigate the impact [...] Read more.
The world is constantly challenged by complex crises—from the COVID-19 pandemic and geopolitical tensions to economic uncertainty and severe environmental disasters. During these critical times, individuals need to reflect on ethical values and demonstrate responsible decision-making, integrity, and preparedness to mitigate the impact of future crises. Education can play an instrumental role in these endeavours. This study presents the user experience and usability evaluation of PREPARED App—an educational mobile application developed to raise users’ awareness on the ethical dimensions of global challenges through real-life case studies. The captivating narratives, clear structure, ease-of-use, and multimedia content were reported as key strengths of the mobile app by both users (n = 54) and experts (n = 4). Suggestions were also captured for enriching the learning experience through enhanced customisation options, personalised feedback mechanisms, and accessibility features. A set of pedagogical guidelines is extracted to enable instructional designers, educators, and mobile application developers to create accessible and engaging mobile learning experiences. Full article
(This article belongs to the Special Issue Human–Computer Interactions and Computer-Assisted Education)
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11 pages, 586 KB  
Article
Executive Functions and Adaptation in Vulnerable Contexts: Effects of a Digital Strategy-Based Intervention
by Alberto Aguilar-González, María Vaíllo Rodríguez, Claudia Poch and Nuria Camuñas
Information 2026, 17(1), 105; https://doi.org/10.3390/info17010105 - 20 Jan 2026
Viewed by 461
Abstract
Childhood and adolescence are critical periods for the development of Executive Functions (EF), which underpin self-control, planning, and social adaptation, and are often compromised in children growing up in psychosocially vulnerable contexts. This study examined the effects of STap2Go, a fully digital, strategy-based [...] Read more.
Childhood and adolescence are critical periods for the development of Executive Functions (EF), which underpin self-control, planning, and social adaptation, and are often compromised in children growing up in psychosocially vulnerable contexts. This study examined the effects of STap2Go, a fully digital, strategy-based EF training, on EF performance and self-perceived maladjustment in 36 at-risk children and adolescents compared with 32 controls. Participants completed pre- and post-intervention assessments using the Neuropsychological Assessment Battery of Executive Functions (BANFE-3) and the Multifactorial Self-Evaluative Test for Child Adaptation (TAMAI). Results showed a significant effect of training on global EF and on General Maladjustment, with improvements only in the intervention group. These findings support the inclusion of scalable, avatar-guided EF stimulation programs such as STap2Go within social inclusion pathways for youth in vulnerable situations. Full article
(This article belongs to the Special Issue Human–Computer Interactions and Computer-Assisted Education)
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18 pages, 2272 KB  
Article
Machine Learning Approaches for Early Student Performance Prediction in Programming Education
by Seifeddine Bouallegue, Aymen Omri and Salem Al-Naemi
Information 2026, 17(1), 60; https://doi.org/10.3390/info17010060 - 8 Jan 2026
Viewed by 922
Abstract
Intelligent recommender systems are essential for identifying at-risk students and personalizing learning through tailored resources. Accurate prediction of student performance enables these systems to deliver timely interventions and data-driven support. This paper presents the application of machine learning models to predict final exam [...] Read more.
Intelligent recommender systems are essential for identifying at-risk students and personalizing learning through tailored resources. Accurate prediction of student performance enables these systems to deliver timely interventions and data-driven support. This paper presents the application of machine learning models to predict final exam grades in a university-level programming course, leveraging multi-modal student data to improve prediction accuracy. In particular, a recent raw dataset of students enrolled in a programming course across 36 class sections from the Fall 2024 and Winter 2025 terms was initially processed. The data was collected up to one month before the final exam. From this data, a comprehensive set of features was engineered, including the student’s background, assessment grades and completion times, digital learning interactions, and engagement metrics. Building on this feature set, six machine learning prediction models were initially developed using data from the Fall 2024 term. Both training and testing were conducted on this dataset using cross-validation combined with hyperparameter tuning. The XGBoost model demonstrated strong performance, achieving an accuracy exceeding 91%. To assess the generalizability of the considered models, all models were retrained on the complete Fall 2024 dataset. They were then evaluated on an independent dataset from Winter 2025, with XGBoost achieving the highest accuracy, exceeding 84%. Feature importance analysis has revealed that the midterm grade and the average completion duration of lab assessments are the most influential predictors. This data-driven approach empowers instructors to proactively identify and support at-risk students, enabling adaptive learning environments that deliver personalized learning and timely interventions. Full article
(This article belongs to the Special Issue Human–Computer Interactions and Computer-Assisted Education)
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18 pages, 1169 KB  
Article
Fusion of Deep Reinforcement Learning and Educational Data Mining for Decision Support in Journalism and Communication
by Weichen Jia and Zhi Li
Information 2025, 16(12), 1029; https://doi.org/10.3390/info16121029 - 26 Nov 2025
Viewed by 726
Abstract
The project-based learning model in journalism and communication faces challenges of sparse multimodal behavior data and delayed teaching interventions, making it difficult to perceive student states and optimize decisions in real-time. This study aims to construct an intelligent decision-support framework integrating educational data [...] Read more.
The project-based learning model in journalism and communication faces challenges of sparse multimodal behavior data and delayed teaching interventions, making it difficult to perceive student states and optimize decisions in real-time. This study aims to construct an intelligent decision-support framework integrating educational data mining (EDM) and deep reinforcement learning (DRL) to address these issues. A bidirectional long short-term memory (Bi-LSTM) network models behavioral sequences, while a conditional generative adversarial network (cGAN) with Wasserstein optimization enhances low-activity student data. The extracted and augmented features are then fed into a Double Deep Q-Network (DQN) to generate adaptive teaching intervention strategies. Experimental results from a 26-week study show that the proposed framework improved personalized learning-path matching from 0.42 to 0.68, increased knowledge mastery from 40.46% to 77.13%, and reduced intervention latency from 210.5 min to 144.6 min. The results demonstrate that the fusion of EDM and DRL can achieve efficient and adaptive decision-making, providing a viable approach for intelligent teaching support in journalism and communication education. Full article
(This article belongs to the Special Issue Human–Computer Interactions and Computer-Assisted Education)
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