Despite artificial intelligence (AI) having transformed major aspects of our society, less than a fraction of its potential has been explored, let alone deployed, for education. AI-powered learning can provide millions of learners with a highly personalized, active and practical learning experience, which is key to successful learning. This is especially relevant in the context of online learning platforms. In this paper, we present the results of a comparative head-to-head study on learning outcomes for two popular online learning platforms (n=199 participants): A MOOC platform following a traditional model delivering content using lecture videos and multiple-choice quizzes, and the Korbit learning platform providing a highly personalized, active and practical learning experience. We observe a huge and statistically significant increase in the learning outcomes, with students on the Korbit platform providing full feedback resulting in higher course completion rates and achieving learning gains 2 to 2.5 times higher than both students on the MOOC platform and students in a control group who don’t receive personalized feedback on the Korbit platform. The results demonstrate the tremendous impact that can be achieved with a personalized, active learning AI-powered system. Making this technology and learning experience available to millions of learners around the world will represent a significant leap forward towards the democratization of education.

A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions

Programming skills are rapidly becoming essential for many educational paths and career opportunities. Yet, for many international students, the traditional approach to teaching introductory programming courses can be a significant challenge due to the complexities of the language, the lack of prior programming knowledge, and the language and cultural barriers. This study explores how large language models and gamification can scaffold coding learning and increase Chinese students sense of belonging in introductory programming courses. In this project, a gamification intelligent tutoring system was developed to adapt to Chinese international students learning needs and provides scaffolding to support their success in introductory computer programming courses.

Leveraging Large Language Model and Story-Based Gamification in Intelligent Tutoring System to Scaffold Introductory Programming Courses: A Design-Based Research Study

Programming skills are rapidly becoming essential for many educational paths and career opportunities. Yet, for many international students, the traditional approach to teaching introductory programming courses can be a significant challenge due to the complexities of the language, the lack of prior programming knowledge, and the language and cultural barriers. This study explores how large language models and gamification can scaffold coding learning and increase Chinese students’ sense of belonging in introductory programming courses. In this project, a gamification intelligent tutoring system was developed to adapt to Chinese international students’ learning needs and provides scaffolding to support their success in introductory computer programming courses. My research includes three studies: a formative study, a user study of an initial prototype, and a computer simulation study with a user study in progress. Both qualitative and quantitative data were collected through surveys, observations, focus group discussions and computer simulation. The preliminary findings suggest that GPT-3-enhanced gamification has great potential in scaffolding introductory programming learning by providing adaptive and personalised feedback, increasing students’ sense of belonging, and reducing their anxiety about learning programming.

Scaffolding CS1 Courses with a Large Language Model-Powered Intelligent Tutoring System

Testing is a significant part of the teaching and learning process. An assessment test has to include test items that are tailored to the individual learning needs of the students in order to be more accurate and support learning in a more effective way. In this paper, a fuzzy-based mechanism is presented for automatic personalized assessment in an e-learning system for computer programming. Particularly, the selection of the most appropriate test items for each individual student is based on a variety of criteria: (i) the student’s knowledge level, (ii) the student’s prior knowledge of computer programming, (iii) the type of programming errors that the student is prone to make, and (iv) the difficulty level of the test items. Linguistic values are used to determine these criteria. Additionally, 45 fuzzy rules are used over these criteria, which imitate the way of thinking of human tutors with regard to deciding about the most appropriate test items that have to be included in an adaptive test. The presented mechanism was used under real conditions and evaluated by experts and students of the Department of Informatics of the University of Piraeus, Greece with very encouraging results. Specifically, both the participating students and experts found that the presented mechanism creates non-repetitive balanced tests that meet learners’ knowledge level and needs.

A fuzzy-based mechanism for automatic personalized assessment in an e-learning system for computer programming

Technological media has become a vital component for educators, enabling greater efficiency in lesson planning and preparation, providing active repositories of student work, as well as more personalised learning for students. The construction of intelligent systems, which encompass the use of these technologies, allow the learning process to be simpler for the student and the teaching process for the teacher to be more efficient. Thus, ambient intelligence will be the element used to connect the classroom to the intelligent classroom and used to adapt the learning process. With the rapid development of the Internet of Things (IoT) and Artificial Intelligence (AI), their applicability in education will be easy to implement and can solve some defects of the traditional education model and the computer-aided system teaching model, and at the same time make learning more motivating and enticing for the student. The aim of this research is to identify the most recent studies on the application of artificial intelligence in education and to make a comparative study of the different models and existing applications that use this technology to promote learning efficiently

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Survey for Smart and Adaptative Education

An Intelligent Tutoring System (ITS) is critical in education because it provides one to one personalized teaching assistance to learners as they educate how to solve problems through guidance and prompt feedback. ITS is one application of Artificial Intelligence (AI) in education. It provides a smart learning environment for students without intervention from the teacher. ITS's primary goal is to support and help learners obtain domain-specific intellectual knowledge in a practical and productive manner through the use of different computing technologies. This paper presents a comprehensive survey for previous research on ITS that utilize various techniques of AI and Machine Learning (ML). It gives an overview of ITS, its architecture, and some existing ITS examples. In addition, it highlights and summarizes the current research efforts and obstacles to ITS using AI, as well as some future opportunities. This study shows the importance of AI and ML in ITS development. It is noticed that researchers focus more on Reinforcement Learning (RL), Artificial Neural Networks (ANN), clustering, Bayesian Network (BN) and Fuzzy Logic (FL) approaches.

Fatema AlShaikh

Papers

AI and Machine Learning Techniques in the Development of Intelligent Tutoring System: A Review