Malaysian Online Journal of Educational Technology

Eyes, Brain, Emotion: AI-Powered Learning Prediction

Duygu Mutlu-Bayraktar, Selcuk Sevgen, Nedime Karakullukcu, Bulent Yilmaz — Sat, 27 Jun 2026 00:00:00 +0200

This study aimed to predict learning performance using machine learning based on learners’ brain waves, eye fixations, and emotional states while studying a multimedia material designed with positive emotional elements. Fifty-nine university students (27 women, 32 men; M = 20.2) participated. During learning, eye movements were recorded with an eye-tracking device, and brain signals were captured via EEG. Learners’ emotional states were measured using the Positive and Negative Affect Schedule, and learning performance was assessed through a retention test. Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) methods were employed to predict learning outcomes. MLR results revealed that positive emotion and fixation were significant predictors of learning performance. Using all input variables, the ANN model achieved high prediction accuracy, suggesting that integrating neurophysiological and affective data can effectively model learning performance in emotionally designed multimedia environments.

Teachers’ Perceptions of ICT Integration in Teaching and Learning and Its Perceived Impact on Academic Performance

Kunzang Tenzin , Sangay Tshewang, Sonam Dorji — Sat, 27 Jun 2026 00:00:00 +0200

The study investigates teachers’ perceptions of integrating Information and Communication Technology (ICT) in teaching, learning, and assessment within Bumthang schools, Bhutan, under the framework of the iSherig ICT Master Plan (2014–2023). With ICT increasingly shaping modern pedagogy through interactive and student-centred approaches, the research aims to evaluate its effectiveness in improving student academic performance and teaching practices while exploring teachers’ attitudes, challenges, and enabling factors. A mixed-methods design combined quantitative surveys and qualitative questionnaires involving n=131 teachers to capture comprehensive insights into ICT use. Findings indicate that teachers widely acknowledge ICT’s positive impact, rating its contribution to learning outcomes at an average of (M=4.1) on a five-point scale. Respondents highlighted that the use of ICT tools in teaching enhances student engagement, simplifies complex concepts, and streamlines assessment processes. Despite these benefits, significant barriers to integration remain, notably inadequate infrastructure, technical disruptions, and insufficient professional training. Nevertheless, 86.3% of teachers expressed a preference for ICT-based teaching over traditional methods, underscoring their readiness for digital innovation in classrooms. The study concludes that while teachers recognise ICT’s transformative role in education, systemic support is critical to harness its full potential. Enhanced investments in digital infrastructure, capacity-building initiatives, and sustained professional development are essential to ensure effective pedagogical integration. These insights provide valuable guidance for policymakers and stakeholders in advancing Bhutan’s vision of inclusive, technologically empowered education that equips learners with the skills and competencies required in a globalized knowledge society.

A Criteria-Based Evaluation of AI-Generated Reading Texts for 5th Grade Students: A Comparative Analysis of ChatGPT, Gemini, and Claude

Elif Akan — Sat, 27 Jun 2026 00:00:00 +0200

The purpose of this study is to examine reading texts at the 5th grade level of middle school produced using ChatGPT 5.2 (OpenAI), Gemini 3.1 Pro (Google DeepMind), and Claude Opus 4.6 (Anthropic) artificial intelligence tools through an evaluation form created based on the Ministry of National Education (MoNE) text qualities (2019) and TTKB electronic content evaluation criteria (2026). In this context, the document analysis method was employed in the study. The data sources for this study consist of six narrative texts generated using three different AI tools. In the evaluation of these documents, an assessment form consisting of 51 items across 7 categories was used. This form was developed by selecting criteria from the MoNE (2019) and Board of Education (2026) guidelines that are appropriate for the characteristics of the story genre. The data collection tool in question was reviewed and approved by two subject matter experts prior to implementation. The evaluations revealed that all three AI tools used in the study largely met the specified criteria, but Gemini received the highest score. It was found that all three AI tools received full marks in the categories of character and content consistency, values and inclusivity, and literary and aesthetic quality. However, no text received a perfect score in terms of language and expression; while punctuation errors were found in all tools, spelling errors were also observed in ChatGPT and Claude. It was concluded that Gemini only produced texts close to the target class level in terms of text length, while Claude lagged behind other tools in terms of visual and text consistency.

The Rise of Artificial Intelligence in Cognitive Behavioral Therapy Research: A Global Bibliometric and Network Analysis

Osman Soner, Betül Tuncer — Fri, 03 Jul 2026 00:00:00 +0200

This study aims to examine scientific output in the fields of cognitive-behavioral therapy (CBT) and artificial intelligence (AI) using bibliometric methods. As part of the study, 1,077 publications from the Web of Science database spanning the years 1989–2026 were analyzed. The study examined annual development trends in the field, the most productive authors, institutions, and countries, as well as key themes and collaboration networks. The findings indicate that research on integrating CBT with artificial intelligence has increased rapidly, particularly after 2015. The United States, Germany, and the United Kingdom were identified as leaders in this field in terms of scientific output. The results of the thematic analysis revealed that, in addition to traditional clinical topics such as depression and anxiety, machine learning, digital therapy, and AI-based interventions are gaining increasing importance. Furthermore, interdisciplinary collaborations are increasing, and research networks are expanding. These findings indicate that AI-supported approaches are assuming an increasingly central role in mental health. The study highlights current trends in the field and provides a guiding framework for future research