This study evaluates “I Learn with Prompt Engineering”, a self-paced, self-regulated elective course designed to equip university students with skills in prompt engineering to effectively utilize large language models (LLMs), foster self-directed learning, and enhance academic English proficiency through generative AI applications. By integrating prompt engineering concepts with generative AI tools, the course supports autonomous learning and addresses critical skill gaps in language proficiency and market-ready capabilities. The study also examines EnSmart, an AI-driven tool powered by GPT-4 and integrated into Canvas LMS, which automates academic test content generation and grading and delivers real-time, human-like feedback. Performance evaluation, structured questionnaires, and surveys were used to evaluate the course’s impact on prompting skills, academic English proficiency, and overall learning experiences. Results demonstrated significant improvements in prompt engineering skills, with accessible patterns like “Persona” proving highly effective, while advanced patterns such as “Flipped Interaction” posed challenges. Gains in academic English were most notable among students with lower initial proficiency, though engagement and practice time varied. Students valued EnSmart’s intuitive integration and grading accuracy but identified limitations in question diversity and adaptability. The high final success rate demonstrated that proper course design (taking into consideration Panadero’s four dimensions of self-regulated learning) can facilitate successful autonomous learning. The findings highlight generative AI’s potential to enhance autonomous learning and task automation, emphasizing the necessity of human oversight for ethical and effective implementation in education.

In this paper we aim to describe the process by which our personalised web-based collaborative teaching/learning methodology (CECIP - Collaboration - Evaluation - Critical thinking - Individual assessment - learner Profile) evolved originating from Vygotsky's theory and based on the (C) collaborative construction of student's knowledge, (E) developing evaluation and assessment skills, (C) developing critical thinking skills, (I) integrating individual evaluation and (P) generating learner profile. Our CECIP methodology integrates individual learning style dimensions and their preferences into e-learning environment by filling out MBTI, Gardner, GEFT and Felder-Silverman questionnaires during our four-semester-research. The paper covers the theoretical foundations of Learning Styles giving analogies to preferred learning strategies. A three-part-research process is described through which the described CECIP model emerged: (1) analysing Learning Styles and Learning Management Systems that claim to support their work; (2) raising the background knowledge of students in cognitive psychology in order to improve design and evaluation methodologies of multimedia learning materials; (3) personalising tasks and assessment based on Bloom's Taxonomy.

Chatbots have gained widespread popularity for their task automation capabilities and consistent availability in various domains, including education. However, their ability to adapt to the continuously evolving and dynamic nature of knowledge is limited. This research investigates the implementation of an internet wizard to enhance the knowledge base of an open-domain question-answering chatbot. The proposed approach leverages search engines, particularly Google, and its features, including feature snippets, knowledge graph, and organic search, in conjunction with data science and natural language models. This mechanism empowers the chatbot to dynamically access the extensive and up-to-date knowledge available on the web, enabling the provision of real time and pertinent answers to user queries sourced from web documents. A pilot study in a higher education context evaluated the chatbot’s mechanism and features, confirming its proficiency in generating responses across a broad range of educational and non-educational topics. Positive feedback and high user satisfaction validate these findings. Notably, the chatbot’s dynamic feature of retrieving related or follow-up questions from search engines significantly enhances student engagement and facilitates exploration of supplementary information beyond the curriculum.

Publisher: Vilnius University Institute of Data Science and Digital Technologies, Journal: Informatics in Education, Title: Aiming at Sustainable Innovation in Teacher Education - from Theory to Practice, Authors: Márta TURCSÁNYI-SZABÓ , The paper composes a framework for learning design, using Web 2.0 technologies in teacher training, transferring the advancement in technology to become an affordance in the teaching/learning process, based on Bloom's Extended Digital Taxonomy in order to enhance the Technological Pedagogical and Content Knowledge of teachers. As a case study, it shows how ELTE University tries to develop sustainable innovation of competencies in digital literacy and modern teaching/learning methodologies directly among the teaching staff in teacher training and student/future teachers as well as indirectly within public education in order to transfer innovation there. The complex aims of a specific course Educational Technology are described with detailed explanation of the methodology used in attaining the prescribed aims, giving links to the concrete tools and resources used. The description of course requirements are tagged with features of the nature of the learning design as being transmissive, dialogic, constructionist and co-constructive, illustrating how each element contributes to the adaptation of theory into practice. The role of a newly established T@T Mentoring Network is explained, which presumes sustainability for innovation within teacher training and the network of in-service and practicing teachers.

Integration of Large Language Models (LLMs) with Learning Management Systems (LMSs) can enhance task automation and accessibility in education. However, hallucination where LLMs generate inaccurate or misleading information remains a challenge. This study introduces the Dynamic Course Content Integration (DCCI) mechanism, which dynamically retrieves course content from Canvas LMS and structures it within an LLM’s context window via prompt engineering, enabling the LLM-powered assistant, Ask ME, to deliver context-aware, curriculum-aligned responses while mitigating hallucinations. A mixed-methods pilot study grounded in Self-Determination Theory (autonomy, competence) and the Technology Acceptance Model (perceived usefulness, ease of use) evaluated DCCI’s effectiveness with 120 first-year programming students at Eötvös Loránd University. The course focused on foundational programming patterns in C#, including writing program specifications. We analyzed 14,746 logged interactions and a post-course survey completed by 101 students. User satisfaction was measured via a 5-point Likert scale (turn-level ratings), while the survey assessed usability, engagement, and ethical concerns. Results indicated high satisfaction (mean 4.65/5) and strong recognition of Ask ME’s ability to provide timely, contextually relevant answers to administrative and course-related queries. 78.06% agreed that Ask ME’s Canvas integration reduced platform switching, improving usability, engagement, comprehension, and topic exploration. Many students reported reduced hesitation to ask questions and increased motivation for self-directed learning, though concerns about over-reliance on AI and reduced student–teacher interaction emerged. This study demonstrates that DCCI enhances LLM reliability, student satisfaction, and engagement in AI-driven educational automation, while highlighting the importance of balancing AI efficiency with pedagogical integrity and human–AI collaboration.

In recent years, chatbot technologies have evolved into modern information and communication technology applications that perform many virtual tasks, including learning. One of the challenges in improving the chatbot is the insufficient knowledge base of chatbots, including education-oriented conversational agents, the challenges in connecting the chatbot with course content on Massive Open Online Course platforms. In this study, a chatbot was developed to answer questions using publicly available technologies, specifically Application Program Interfaces (APIs) that promise convenient user accessibility via APIs, such as the Facebook Messenger platform along with wit.ai API, Canvas MOOC API, and Wikipedia API. API technologies were used to connect the chatbot to selected course content on the MOOC platform as well as to large knowledge bases such as Wikipedia to expand the knowledge base of the Conversational Agent. The course selected for the chatbot integration was on general informatics topics. Most course participants interacted with the chatbot via the Facebook Messenger platform using their handheld devices. Thus, integrating the chatbot into a widely used platform such as Facebook Messenger is a convenient and effective way for reaching learners. The API technology enabled an efficient connection between the chatbot and third-party apps, including the Messenger app, wit.ai, Canvas MOOC, and Wikipedia. This was due to the variety, richness, manipulation capabilities, and format of data that an API can transfer. In addition, the Wikipedia API seemed to be a vast source of information for expanding the chatbot's knowledge base. Not all of the queries posed to the chatbot were part of the course content. Some participants questioned the personality of the chatbot and were curious about the persona of the conversational agent. This suggests that a chatbot that has been endowed with some personality traits is stimulating and more likely to be accepted by learners.