Publisher: Vilnius University Institute of Data Science and Digital Technologies, Journal: Informatics in Education, Title: A Method for Automated Program Code Testing, Authors: Sigitas DRASUTIS, Vida MOTEKAITYTE, Algirdas NOREIKA , The Internet has recently encouraged the society to convert almost all its needs to electronic resources such as e-libraries, e-cultures, e-entertainment as well as e-learning, which has become a radical idea to increase the effectiveness of learning services in most schools, colleges and universities. E-learning can not be completely featured and met without e-testing. However, in many cases e-testing tools are suitable just for traditional/theoretical knowledge testing, covered by such items as questions, quizzes, matching boxes and other. The article ``A Method for Automated Program Code Testing'' tackles the lack of functions in e-testing systems and suggests e-assessment possibilities for students who study computer science, especially programming. The article analyzes the method that allows freely entering answers to questions, checking program syntax during the testing and enables automatic written code checking and evaluation.

Publisher: Vilnius University Institute of Data Science and Digital Technologies, Journal: Informatics in Education, Title: A Method for Rational Provision of Learning Syllabus, Authors: Vida DRASUTE, Sigitas DRASUTIS, Dalia BAZIUKE , Distance, e-education supported by technological development have enhanced educational processes by combining achievements from various scientific fields. This techno-educational enhancement has happened within the last decade. Virtual learning has grown in popularity as a new way of learning that gives preference to learn in convenient time and places, using specially prepared learning materials. The progression of technological developments implies the rise of user demands. It also aspires to look for flexible, but qualitative, training and learning ways. One of the outcomes is adaptive and intelligent learning environments that show to become a common tool used for virtual learning. This paper focuses on the key feature elements of adaptive learning environments (ALE), also researching about intelligent agents and their behaviour in ALEs. The method to provide individual learning syllabus according student knowledge in rational way is proposed. It is based on latent semantic indexing algorithm and is featured with an ability to update syllabus according individual learning outcomes of the student. This is yet another tool in a teacher's tool box. The description of results obtained during the exploration of proposed method is presented as well.

Artificial intelligence (AI)-based support systems are transforming the educational landscape by enhancing teaching efficiency, personalized learning, and accessibility. Despite rapid technological progress, educational institutions face persistent challenges such as unequal access to quality learning resources, limited teacher support, and the need for individualized student engagement. These issues hinder effective learning outcomes and inclusivity in modern classrooms. This study presents a comprehensive literature review and a methodology grounded in constructivist learning theory to develop an AI-based educational support framework. The study is situated within the context of a higher education course integrating AI-supported learning. The proposed framework is developed by synthesizing theoretical and empirical evidence and is subsequently evaluated by experts in educational technology and artificial intelligence. Data are collected through structured expert questionnaires and qualitative feedback. Quantitative data are analyzed using descriptive statistics, while qualitative responses are examined through thematic analysis to inform framework refinement. The study adheres to established ethical principles, including informed consent, voluntary participation, confidentiality, anonymity, and secure data management. Moreover, the paper explores the design and implementation of sustainable and AI-based educational support systems that address these challenges through intelligent tutoring, adaptive learning analytics, and automated feedback mechanisms. By integrating natural language processing, machine learning, and predictive modelling, the proposed framework provides real-time assistance to educators and learners, fostering data-driven decision-making and inclusive pedagogy. Qualitative expert evaluation suggests that an AI-based educational support framework has the potential to improve teaching support, learner engagement, and personalized learning while providing a scalable and equitable approach for higher education.

This study analyses the application of games and gamification to increase student motivation and ensure an effective and sustainable learning process in the modern sustainable environment of digitalisation and e-learning. Recent changes have revealed that traditional teaching methods do not always sufficiently engage students, increasing the risk of decreased motivation to learn and poorer achievement. Gamification, which is based on the integration of game design elements – points, levels, badges, challenges – into learning activities, can help reduce these risks by promoting student autonomy, a sense of competence and social engagement. The study presents the application of a gamification system based on case analysis using an educational platform that allows the creation of educational games and the assessment of student engagement in real time. The results show that gamification methods significantly increase student activity and motivation to learn, while integrated game elements improve student’s motivation and learning experience. Based on the analysis, the paper presents conclusions and recommendations for the effective, high-quality, and sustainable organisation of the modern education process based on gamification.

Artificial Intelligence (AI) based support systems are transforming the educational landscape by enhancing teaching efficiency, personalized learning, and accessibility. Despite rapid technological progress, educational institutions face persistent challenges such as unequal access to quality learning resources, limited teacher support, and the need for individualized student engagement. These issues hinder effective learning outcomes and inclusivity in modern classrooms. This paper explores the design and implementation of sustainable and AI-based educational support systems that address these challenges through intelligent tutoring, adaptive learning analytics, and automated feedback mechanisms. By integrating natural language processing, machine learning, and predictive modelling, the proposed framework provides real-time assistance to educators and learners, fostering data-driven decision-making and inclusive pedagogy. Qualitative research demonstrates that AI-driven systems can significantly improve academic performance, teacher productivity, and learner motivation, offering a scalable and equitable solution for the future of education in both traditional and digital environments.