2026· International Journal of Information and Education Technology· Vol 16, pp. 2068-2078· 0 citations· 17 references
TL;DR
The study contributes user-derived design requirements that can guide the development of trustworthy and context-appropriate AI-supported learning platforms for undergraduate ICT students in programming-related courses at the two participating universities; broader generalization to other higher education fields requires further research.
Abstract
This study examines undergraduate Information and Communications Technology (ICT) students’ needs, expectations, and concerns regarding Artificial Intelligence (AI)-supported adaptive learning for programming education in Kazakhstan. Using a mixed-methods descriptive design, data were collected from 134 students at two universities via a multilingual online questionnaire that combined Likert-scale items with open-ended questions. Quantitative results indicate high awareness of AI and strong readiness to use AI-supported learning tools, with the highest-rated needs focusing on adaptive content, automated practice/task generation, progress tracking, and mobile access. At the same time, students reported moderate concerns about the accuracy of AI-generated materials and the privacy of learning data, and they preferred models where teachers remain actively involved in interpreting and guiding AI feedback. Qualitative findings reinforced these patterns, identifying technical reliability, language/localization, transparency, and integration with existing Learning Management System (LMS) as key barriers, while emphasizing stability, explainable feedback, multilingual support, and teacher-in-the-loop functionality as priorities for improvement. This study should be interpreted as a pre-implementation needs assessment based on students’ responses to a hypothetical AI-supported learning scenario and does not provide evidence of actual adoption or instructional effectiveness. The study contributes user-derived design requirements that can guide the development of trustworthy and context-appropriate AI-supported learning platforms for undergraduate ICT students in programming-related courses at the two participating universities; broader generalization to other higher education fields requires further research.
. The advancement of information and communication technology has driven innovation in higher education, particularly in intelligent learning systems. This study develops an Artificial Intelligence (AI) – based e-learning system to improve learning in the Educational Management Graduate Program at Universitas Negeri Medan. Using a Research and Development (R&D) approach with the ADDIE model, data were collected through interviews, surveys, and curriculum analysis involving lecturers and graduate students. The system integrates adaptive learning recommendations, automated feedback, learning analytics, and an NLP-based chatbot. Results show significant improvements, including increased learning efficiency (58%), faster feedback response time (65%), and high user satisfaction (89%). Interactivity and student motivation also improved by 54% and 61%, respectively. Expert validation through Focus Group Discussion (FGD) confirmed strong system feasibility (92%) and pedagogical relevance (85%). These findings indicate that AI-based e-learning enhances personalization, engagement, and data-driven learning in higher education.
Ngatmini Ngatmini, Baharuddin Baharuddin, M. As et al.· Proceedings of the 10th Annu...· 0 citations
Particularly in science and math classes where students struggle with abstract thinking, conceptual comprehension, problem-solving, and sustained academic engagement, Artificial Intelligence (AI) has grown in importance as a component of instructional technology. As a result, AI-enabled digital solutions provide chances for data-driven teaching support, adaptive feedback, and personalized learning pathways. With a focus on conceptual comprehension, problem-solving skills, academic engagement, and self-directed learning, this research sought to determine the degree to which AI-powered digital tools improve students’ learning outcomes in science and mathematics. The study used a mixed-methods approach, combining qualitative information from student comments and classroom observations with quantitative assessments of academic progress. The research was conducted in upper secondary and postsecondary educational environments. Science and math classrooms are using AI-supported technology like data-driven instructional dashboards, intelligent feedback mechanisms, and adaptive learning systems. While qualitative data offered insight into student participation, classroom interaction, and perceived learning assistance, quantitative data were used to evaluate students’ learning performance before and after the intervention. The results show that using AI-supported digital tools enhanced learning outcomes, especially in courses that call for gradual conceptual expansion, abstract cognition, and iterative reasoning. Additionally, qualitative data indicated that students benefited from increased autonomy during learning activities, timely feedback, ongoing observation, and personalized learning paths. The research comes to the conclusion that, when paired with curriculum goals and efficient instructional design, AI-supported digital tools should be seen as cognitive and pedagogical support systems rather than just technical advancements that might enhance science and math teaching.
Edwin Gerardo Acuña Acuña· Journal of Computers for Sci...· 0 citations
Preparing future educators for technology-enhanced learning environments has become increasingly important as artificial intelligence (AI) continues to influence teaching and learning. Guided by the Intelligent Technological Pedagogical Content Knowledge (i-TPACK) framework, this mixed-methods study examined preservice teachers’ perceptions of AI and the competencies they considered necessary for effective AI integration. Participants included 108 preservice teachers enrolled in a teacher preparation program at a public university in the southeastern United States. Data were collected using a survey containing Likert-scale and open-ended questions. Quantitative data were analyzed using descriptive statistics, exploratory factor analysis, and independent-samples t-tests, while qualitative responses were analyzed using thematic coding. The exploratory factor analysis identified a four-factor empirical structure that partially corresponded with the original theoretical domains, with varying levels of internal consistency. Preservice teachers generally viewed AI favorably and recognized its potential to support teaching and learning. Participants with internship experience reported significantly higher scores for perceived changes brought by AI and reasons for using AI than those without internship experience. Qualitative findings identified five competencies considered important for responsible AI integration: AI literacy, prompt engineering, critical evaluation, ethical AI use, and pedagogical balance. Participants also expressed concerns about academic dishonesty, misinformation, overreliance on AI, reduced critical thinking, and loss of human interaction. The findings highlight the importance of preparing preservice teachers to integrate AI in pedagogically meaningful and ethically responsible ways.
Aslihan Unal, John Hobe· Education sciences· 0 citations
Artificial intelligence has become a common learning aid in higher education, yet its contribution to motivation may coexist with risks to students’ cognitive autonomy. This study examined descriptive patterns in the influence of AI-based learning on the learning motivation of students enrolled in Computer Technology programs at universities in West Java, Indonesia. A descriptive quantitative design was applied to questionnaire responses from 30 students selected through simple random sampling as reported in the original study. The instrument contained six Likert-scale statements covering personalized learning, feedback speed, access to learning resources, critical thinking, independent creativity, and self-confidence. The analysis combined agree and strongly agree responses and converted them into percentages. The strongest positive response concerned easier access to and summarization of learning resources (90%), followed by rapid feedback (87%) and personalized understanding (83%). Moderate risk patterns also appeared, including reduced critical thinking (63%), lower confidence because of repeated AI checking (60%), and reduced independent creativity (57%). The findings indicated that AI-based learning supported motivation through efficiency, accessibility, and perceived competence, but unguided dependence could weaken independent reasoning. Lecturers should design process-oriented assessments, require verification of AI output, and maintain direct academic interaction. Because the study used a small sample and descriptive analysis, the results should be treated as preliminary rather than causal.
Dodi Syaripudin· Qriset Indonesia Journal of...· 0 citations
The findings show that AI is increasingly seen as a transformative academic tool, especially for research, language learning, writing support and problem‐solving, and despite widespread AI adoption, the study identifies significant gaps in institutional infrastructure and the absence of systematic training programmes.
M. Doğan, B. Kashkhynbay, Zhaniyat Baltabayeva· European Journal of Educatio...· 0 citations
The rapid integration of artificial intelligence (AI) in higher education has increased the demand for adaptive Learning Management Systems (LMS) capable of delivering personalized learning at scale. However, empirical studies covering the full lifecycle of designing, prototyping, and testing AI-powered LMS remain limited, particularly in developing regions. This study aimed to design, prototype, and test EduVa LMS (Education Virtual Assistant), an AI-powered interactive LMS with adaptive modules and AI-driven assessments, implemented across ten private universities in East Nusa Tenggara, Indonesia. A Design Science Research (DSR) approach integrated with the ADDIE model guided the development process. A total of 360 participants were involved, including experts, students, and lecturers. Validation results showed a Content Validity Index (CVI) of 0.80, confirming content validity. Usability testing using the System Usability Scale (SUS) yielded mean scores of 82.4 (students) and 85.8 (lecturers). Adaptive performance analysis indicated 82.1% assessment accuracy, 3.7 content adjustments per session, and an 87.9% completion rate. A strong correlation (r = 0.81, p < .01) was found between AI assessments and learning objectives. The findings demonstrate that the EduVa LMS is valid, usable, and effective for implementing adaptive learning.
Sumarlin Sumarlin, Skolastika Siba Igon, R. N. Naatonis et al.· Indonesian Journal of Educat...· 0 citations