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Intelligent learning systems provide relevant learning materials to students based on their individual pedagogical needs and preferences. However, providing personalized learning objects based on learners’ preferences, such as learning styles which are particularly important for the recommendation of learning objects, re-mains a challenge. Recommen...
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... taking the post-test, learners are asked the following questions: "Q1: Were the recommendations provided by our chatbot LearningPartnerBot helpful in your learning process?"; "Q2: How would you rate the overall experience with LearningPartnerBot?", to find out their satisfaction towards the recommendations based on their learning style and their satisfaction towards the chatbot. Table 1 shows the distribution of responses to each question. Based on these results, we conclude that our chatbot LearningPartnerBot was perceived as interesting and helpful, by providing learners with personalized recommendations of learning objects according to each learner's learning style. ...Similar publications
Adaptive learning is a methodology that allows to identify the level of students’ knowledge and their learning styles and transform materials, tasks and ways of their delivery according to the needs of learning process participants. The interest of higher education institutions (HEI) to use adaptive learning as an innovative datadriven approach to...
Citations
... With the increasing demand for self-regulated learning, authors of [6,12] emphasized the need for research on artificial intelligence settings in education, which has opened a new avenue for constructing chatbots. [13] confirmed that the use of chatbots in education has the potential to significantly improve students' satisfaction and learning outcomes. [5,8] highlighted the numerous advantages that are related to the incorporation of chatbots into online learning, such as enhancing students' engagement and motivation. ...
Artificial Intelligence (AI) chatbots play an important role in modern
izing education, particularly e-learning platforms by acting as intelligent tools to
address issues such as disengagement and dropout rates. AI chatbots are also used
to captivate students through engaging and enjoyable learning activities. These
chatbots can enhance the pedagogical concept of Self-Regulated Learning (SRL)
by providing personalized support and feedback to students. This work explores
how AI chatbots might increase students’ SRL skills and foster successful learning
outcomes in e-learning environments. Our proposed conceptual framework aims
to link the AI chatbots (technological side) and self-regulated learning (pedagogi
cal side) using the power of academic emotions. In the forethought phase of SRL,
our AI chatbot will guide students in setting short-term goals while detecting their
initial motivation and then monitor students in the following phases of the SRL
process. The framework highlights how AI chatbots using academic emotions can
be used to support learning needs and will serve as a roadmap for future theory
development and empirical investigation.
... Their findings emphasised the positive impact of these chatbots on students' academic performance, highlighting the effectiveness of AI-enhanced environments accompanied with conventional educational methodologies. Kaiss et al. [6] have introduced an adaptive chatbot called LearningPartnerBot integrated within Moodle, with interesting personalization techniques which adapt content according to the student's learning style, significantly improving learning outcomes. Following this, Santana et al. [7] developed a chatbot to assist university students by answering fundamental and administrative questions. ...
... • Personalized Learning Experience: Similar to the LearningPartnerBot presented by Kaiss et al. [6], Moodle-GPT provides personalized assistance tailored specifically to the course materials, enhancing the educational experience through context-related interactions. However, unlike LearningPartnerBot, our chatbot distinctly prioritizes collaborative and community-based dialogues rather than focusing only on individual learning. ...
As generative artificial intelligence (GAI) continues to advance in education, nearly achieving a true one-to-one teacher-student ratio, it also raises significant concerns about the potential extinction of traditional learning methods. Because of the non-judgmental environment AI provides, many students hesitate to ask questions in front of their peers, relying instead on AI's ability to deliver accurate answers and clear explanations. The rise of AI and its development in personal tutoring cannot and should not be stopped, but the decline of group learning, friendly debates, and collaborative discussions can be delayed. This paper introduces a method for integrating Moodle, a widely used learning management system, with generative AI tailored to a specific course's materials. This approach not only suggests but also actively promotes group discussions. The outcome of this research is a platform where students can ask questions and receive nearly instant responses, with the assurance that any inaccuracies will be corrected quickly. At the same time, this enables other students to learn from and contribute to the discussion.
... Intelligent tutors or agents offer personalized instruction and adaptive feedback, adjusting the complexity and pacing of material to match the student's progress (Nguyen et al., 2019). Machine learning algorithms analyze educational data to uncover patterns and insights, which can inform personalized learning paths and improve educational outcomes (Kaiss et al., 2023). Personalized learning systems adapt content and assessments to the learner's individual needs, optimizing their engagement and retention (Belda-Medina & Calvo-Ferrer, 2022). ...
Mastering mathematics is often challenging for many students; however, the rise of artificial intelligence (AI) offers numerous advantages, including enhanced data analysis, automated feedback, and the potential for creating more interactive and engaging learning environments. Despite these benefits, there is a need for comprehensive reviews that provide an overview of AI's role in mathematics education to help educators identify the best AI tools, and to inform researchers about current trends and future directions. This study conducts a systematic literature review (SLR) to investigate the applications and trends of AI in mathematics education by examining articles published in reputable journals indexed in Web of Science and Scopus. The review categorizes AI tools into those narrowly addressing mathematical problems, such as solving equations and visualizing geometry, and those offering broader pedagogical support, including adaptive learning systems and generative AI platforms. Key aspects analyzed include the distribution of AI in Mathematics Education (AIME) studies across different educational levels, the types and categories of AI tools used, the functionality of commercialized AIME tools available on the internet, and the emerging trends and future directions in AIME based on recent literature. The insights from this SLR are crucial for educators, policymakers, and researchers, enabling them to integrate AI effectively into mathematics education and tailor tools to specific teaching strategies and learning needs.
... AI-based chat-bots for different teaching and learning purposes is one of the most popular AI-solutions for Moodle. W. Kaiss, K. Mansouri, F. Poirier [19,20] address chat-bots as a tool for building up adaptive learning recommendations based on the level of knowledge and learning style. Y.-A. ...
Artificial intelligence (AI) is one of the most prevalent topics in modern science. It is also reflected in the higher education sphere. Implementation of AI in university activities requires prior analysis of spheres where artificial intelligence can be used to provide responsible and smart use of AI. Based on the survey of educational process participants, the authors analysed the level of readiness of respondents to AI utilisation, their apprehension of AI’s role in different spheres of university activities. The results of the survey have shown that most educational process participants either do not use artificial intelligence or use a very limited number of resources. Still the respondents estimated the possible impact of AI on different spheres of university activities as moderate or high. The spheres of open university ecosystem where AI can be used, were singled out and presented as a structural model. Among the spheres of university activities where AI can be implemented there are infrastructure, security, management and administration, research, ratings, sustainable development, learning personalisation, and e-learning. The selection of artificial intelligence tools for each sphere was performed and presented. Challenges of AI implementation are discussed, among which there are data security, privacy, ethical and legal issues, bias of AI, requirements to technical knowledge of university staff and university infrastructure, teachers’ resistance, depersonalization of education, risks of education quality decrease. The advantages of AI implementation in each of the defined spheres are described. The need to document the rules of AI utilization at HEIs is stressed. The results of the research can be used for planning AI implementation in higher education institutions and AI policy formation.
... No obstante, la implementación efectiva de la personalización del aprendizaje enfrenta desafíos, como la necesidad de capacitación docente y recursos adecuados, aspectos que Kaiss et al. (2023) identifican como críticos para el éxito de estas iniciativas. En última instancia, la personalización ...
La convivencia democrática en las aulas contribuye al desarrollo integral de los estudiantes y la construcción de una sociedad justa. Esta investigación analizó las transformaciones en prácticas pedagógicas y dinámicas escolares tras la pandemia COVID-19. Utilizando un enfoque cualitativo con diseño fenomenológico-hermenéutico, se realizaron entrevistas semiestructuradas a cuatro expertos docentes. Los resultados revelaron que las estrategias de adaptación pedagógica, incluyendo la integración tecnológica y enfoques de aprendizaje activo, mejoraron la participación estudiantil y el pensamiento crítico. Las dinámicas de diálogo, caracterizadas por la escucha activa y la creación de espacios seguros, fomentaron un ambiente inclusivo. Los desafíos institucionales impulsan una gestión de recursos y una capacitación docente multidimensional. Se concluyó que estas adaptaciones fortalecen el entorno educativo, promoviendo valores, el pensamiento crítico y autonomía estudiantil. Se recomienda implementar un modelo de gestión escolar participativa para fomentar la convivencia democrática y el compromiso cívico en toda la comunidad educativa en Lima.
... AI-driven systems can analyze vast amounts of data to identify patterns in student behavior and adapt learning materials accordingly. For instance, intelligent learning systems can recommend learning objects based on the Felder-Silverman Learning Styles Model, as demonstrated by the LearningPartnerBot integrated into the Moodle platform, which personalizes recommendations and answers learners' questions in real-time (Kaiss et al., 2023). AI's role in education extends to adaptive testing, intelligent tutoring systems, and learning analytics, which collectively aim to improve student learning outcomes by adapting the educational experience to each student's unique needs (Tiwari, 2023). ...
This study presents a scientometric analysis of personalized learning research from 2020 to 2024. Using the Lens database, 4,463 scholarly articles were analyzed to identify key trends and patterns in this rapidly evolving field. The analysis revealed consistent publication growth over the 5 years, with journal articles dominating as the primary publication type. Hessian Normal University emerged as the most productive institution, while Jackson Steinher was identified as the most prolific author. The United States and China were the leading countries in terms of research output. Education and Information Technologies was the top journal publishing personalized learning research. Co-authorship network analysis highlighted collaborative patterns among researchers, while keyword co-occurrence networks revealed the centrality of artificial intelligence and related concepts in the field. Citation analysis identified influential documents and sources shaping the discourse. The findings suggest an increasing focus on integrating AI and machine learning into personalized learning systems and a growing emphasis on interdisciplinary approaches. This scientometric overview provides valuable insights into the current state and emerging trends in personalized learning research, which may inform future studies and applications in this domain.
... Recent studies (Hume et al., 2022) have shown that chatbots have the capacity to significantly improve student participation levels and enhance their learning performance. These technologies help learners to learn more effectively by supporting diverse learning styles (Kaiss et al., 2023;Rajkumar & Ganapathy, 2020) and providing access to extensive information to support learning for both instructors and learners. In addition, AI systems can simplify the subject matter by breaking it into smaller units that students find easier to understand (Rossi et al., 2011) or deliver content at a pace that matches the ability of individual learners and provide just-in-time learning support and assistance. ...
Junior Secondary School (JSS) or middle school education is peculiar as it involves the introduction of a wide array of subjects across the sciences, arts, humanities, business and vocational fields to young learners. This situation can be ovewhelming, resulting in high cognitive load (CL), with consequent poor learning outcomes, and other negative issues including high dropout rates, requiring urgent attention. AI tools have been explored for addressing multiple learning issues. AI chatbots are particularly useful based on their ability to support individualized learning, pre-training, and other concepts that can facilitate CL management. This study evaluated the impact of an AI-based chatbot system for reducing students' CL and improving learning outcomes, attitude and retention among JSS students. A quasi-experimental study with 120 students was conducted over an 8 week period with 24 learning sessions. The experimental group (N=60) learnt using 'iLearnTech', an AI Chatbot developed specifically for the study. The control group (N=60) learnt through the traditional approach with no chatbot. Learning content was based on the JSS Basic Technology education, a precursor to TVET. Data was collected using the Cognitive Load Measure, the Basic Technology Achievement Test, Students' Attitude Survey (SAS), and Students' Retention Test. The experimental group exhibited huge reductions in CL and corresponding improvements in learning outcomes, attitude and retention. The results also confirmed known relationship between the dependent variables and highlights the potential of AI powered educational tools for addressing diverse educational issues including promoting equitable access, and sustainable education in developing nations and resource-constrained environments. This work contributes to ongoing discussions on AI applications in education. Its novelty lies in its exploration of AI technology in addressing CL issues in the context of junior secondary education. Implications for educational policy and practice, particularly curriculum design and e-learning integration are highlighted.
... In addition, it assists students in focusing their efforts through its ability to perform assessments and provide feedback on their progress [59][60][61]. However, its usefulness depends on proper integration into teaching plans, which requires additional research to maximize its impact [27,62]. ...
Background
The use of artificial intelligence tools, such as ChatGPT, is on the rise in nursing education. In the field of healthcare, ChatGPT can offer unique opportunities to enhance the learning and clinical practice of nursing students. However, it is still necessary to explore how this tool affects students' performance and perception in their nursing education.
Objective
The objective of this study was to evaluate the impact of ChatGPT on nursing students' education and determine how it influences their learning outcomes.
Design
This study employed a quantitative cross-sectional design.
Setting
The study was conducted in the Bachelor of Nursing program at the University of León, Spain.
Participants
Ninety-eight nursing students enrolled in the Nursing Care and Services Management course during the second semester of 2024 participated in the study.
Methods
Data were collected using three validated questionnaires that assessed sociodemographic characteristics, knowledge of artificial intelligence, and perceptions of using ChatGPT as an educational tool. The data were analyzed using IBM SPSS Statistics, version 29.1.
Results
Students who used ChatGPT showed a significant improvement in their academic grades (p < 0.05). Additionally, 89.5 % of the students reported significant improvements in their academic performance. Women perceived ChatGPT as especially useful for completing academic tasks (85.14 % versus 50.00 % in men, p = 0.003). A positive correlation was observed between prior use of ChatGPT and GPA (ρ = 0.240, p = 0.026).
Conclusions
ChatGPT is a valuable tool that enhances the learning and satisfaction of nursing students. Its integration into nursing education programs not only boosts academic performance but also promotes the adoption of technological innovations in professional training. Continuous incorporation of AI tools in education is recommended to improve academic outcomes and prepare students for evolving healthcare environments.
... To address these issues, there is a need for a physics teaching module as a learning resource that can foster student independence, stimulate and enhance their enthusiasm for learning, and align with the requirements or aspects of the Merdeka Curriculum. The application of teaching modules in physics education can be implemented through project-based activities, which provide students with broader opportunities to actively explore physics concepts, contribute to character development, and achieve the goals outlined in the Pancasila Student Profile (Dewi & Safitri, 2023;Husnadi et al., 2024;Kaiss et al., 2023). Given this context, it is time to develop a new approach to the learning process. ...
This research is a type of Research and Development (R&D) study utilizing the 4D model development method (Define, Design, Development, and Dissemination). The study was conducted at MAN 1 Buton Selatan with the aim of designing and analyzing a valid, practical, and effective Merdeka flow-based physics teaching module that can improve student learning outcomes. The developed Merdeka flow-based physics teaching module was then tested on 25 students of class X-1 and evaluated by 10 practitioners or physics teachers from Siompu District and West Siompu District, Buton Selatan. The instruments used in this study included a validation sheet for the physics teaching module, practitioner assessment questionnaires, and a validated student learning outcomes test. The results showed that the Merdeka flow-based physics teaching module at MAN 1 Buton Selatan was valid, with an Aiken's V value of 0.74, practical, with a practitioner response score of 92.44%, and effective, with 80% of students showing a high category improvement in physics learning outcomes based on the N-gain calculation. Based on the results, the Merdeka Flow-based physics teaching module is overall considered valid, practical, and effective for use in the physics learning process.
... By recognizing and responding to a learner's strengths and weaknesses, the chatbot can create a personalized learning path that maximizes engagement and retention, leading to more effective language acquisition. (16) In the realm of ESL, AI chatbots can adapt to a learner's style by offering a variety of learning activities. For visual learners, the chatbot might use images, charts, and diagrams to illustrate vocabulary or grammar points. ...
The integration of artificial intelligence (AI) in language teaching has emerged as a transformative approach, particularly in the realms of English as a Second Language (ESL) and Chinese as a Foreign Language (CFL). This article explores the potential of AI chatbots as effective tools for enhancing language acquisition. By examining the current landscape of AI in language education, we identify the unique benefits that chatbots bring to the learning process, including personalized interaction, immediate feedback, and continuous engagement. The article delves into the design and implementation of AI chatbot systems tailored for ESL and CFL contexts, highlighting their role in vocabulary development, grammar practice, and conversational skills. Furthermore, it addresses the challenges and limitations of using chatbots in language teaching, proposing strategies for overcoming these obstacles. Through case studies and empirical data, the article demonstrates how AI chatbots can be harnessed to create a dynamic and interactive learning environment that caters to the diverse needs of language learners. Ultimately, this work advocates for the thoughtful integration of AI chatbots to complement traditional teaching methods, thereby paving the way for more effective and accessible language education
