Keyword: personalized learning
In this analysis, we review artificial intelligence (AI)-supported personalized learning (PL) systems, with an emphasis on pedagogical approaches and implementation challenges. We searched the Web of Science and Scopus databases. After the preliminary review, we examined 30 publications in detail. ChatGPT and machine learning technologies are among the most often utilized tools; studies show that general education and language learning account for the majority of AI applications in the field of education. Supported by particular learning approaches stressing student characteristics and expectations, the results show that automated feedback systems and adaptive content distribution define AI’s educational responsibilities mostly. The study notes major difficulties in three areas: technical constraints and data privacy concerns; educational and pragmatic barriers. Although curriculum integration and teacher preparation are considered major concerns, pedagogical challenges come first above technology integration. The results also underline the need for thorough professional development activities for teachers and AI tools for especially targeted instruction. The study shows that the efficient application of AI-enabled PL requires a comprehensive strategy addressing technological, pedagogical, and ethical issues all at once. These results help to describe the current state of AI in education and provide ideas for future developments as well as techniques for its use.
Keywords: artificial intelligence, personalized learning, adaptive learning, intelligent tutoring systems, ethics in AI education
Online teaching during the COVID-19 pandemic compelled many instructors to seek efficient and effective ways to stay connected with their students and improve the learning experience by using a wide range of available technologies. This multiple-case study, in three South-East Asian universities, investigated whether the use of technology in university teaching and learning during that period influenced personalized learning, and if so, how. The study also explored the kinds of institutional support for teachers and learners that led to increased technology-enhanced personalized learning (TEPL). Using a qualitative approach, the study analyzed 23 individual interviews and 3 document analyses (circulars, announcements, etc.), involving six administrators (AD), six faculty developers (FD), and eleven instructors. Purposeful sampling targeted AD involved in policy development and strategic planning, FD responsible for professional development programs, and instructors with high teaching evaluation scores and expertise in online learning across various disciplines. Thematic analysis revealed that technology enhanced flexibility in learning pace, time, and place, increased student choice in learning methods, enabled needs-driven teaching adjustments, and provided more and broader personalized feedback, sometimes facilitated by anonymity. The provision of training and resources, including emotional, physical, and infrastructure support for students, facilitated the growth of TEPL. The significance of this study lies in discussing how online teaching, and institutional support for it, facilitated the growth of TEPL. Universities can explore collaborations to further advance this growth.
Keywords: technology-enhanced personalized learning, online teaching and learning, instructors, faculty developers, administrators, university support
Artificial intelligence (AI) introduces new tools to the educational environment with the potential to transform conventional teaching and learning processes. This study offers a comprehensive overview of AI technologies, their potential applications in education, and the difficulties involved. Chatbots and related algorithms that can simulate human interactions and generate human-like text based on input from natural language are discussed. In addition to the advantages of cutting-edge chatbots like ChatGPT, their use in education raises important ethical and practical challenges. The authors aim to provide insightful information on how AI may be successfully incorporated into the educational setting to benefit teachers and students, while promoting responsible and ethical use.
Keywords: artificial intelligence, education, chatbots, ChatGPT, personalized learning
Personalized learning (PL) has been internationally promoted as a future direction of educational reform efforts. While there is growing evidence of PL enhancing learning outcomes, teachers reported having difficulty envisioning PL in practice. This national survey study investigated how PL is practiced in K-12 learner-centered schools in the U.S. to inform educators of learner-centered teachers’ PL practice and identify gaps between their practice and research. Five essential components were identified: PL plans, competency-based student progress, criterion-referenced assessment, project- or problem-based learning, and multi-year mentoring. Based on the five components, we identified 308 learner-centered schools and received 272 teacher responses from 41 schools. The five components were implemented with different levels of implementation fidelity. We uncovered several areas in need of improvement. Career goals were not often considered when creating PL plans. A misalignment between student progress and assessment practice was found. There was a lack of community involvement during the PBL process. Teachers were not able to build a close relationship with all students. These findings from learner-centered schools revealed that paradigm change demands continuous effort to transform all aspects of the educational system. Suggestions are made for practice and future research.
Keywords: personalized learning, competency-based student progress, project-based learning, problem-based learning, multi-year mentoring