We presented two empirical studies to assess the efficacy of two Deep Reinforcement Learning (DRL) frameworks on two distinct Intelligent Tutoring Systems (ITSs) to exploring the impact of Worked Example (WE) and Problem Solving (PS) on student learning. The first study was conducted on a probability tutor where we applied a classic DRL to induce…

A key challenge in e-learning environments like Intelligent Tutoring Systems (ITSs) is to induce effective pedagogical policies efficiently. While Deep Reinforcement Learning (DRL) often suffers from "sample inefficiency" and "reward function" design difficulty, Apprenticeship Learning (AL) algorithms can overcome them.…

This research suggests a methodology to examine the effectiveness Artificial Intelligence (AI) on the cognitive abilities of college students so that future researchers can utilize this experimental project to focus on how AI-powered Intelligent Tutoring Systems (ITSs) affect learning outcomes. As AI continues to revolutionize all walks of life,…

Large Language Models (LLMs) hold promise as dynamic instructional aids. Yet, it remains unclear whether LLMs can replicate the adaptivity of intelligent tutoring systems (ITS)--where student knowledge and pedagogical strategies are explicitly modeled. We propose a prompt variation framework to assess LLM-generated instructional moves' adaptivity…

Think-aloud protocols are a standard method to study self-regulated learning (SRL) during learning by problem-solving. Advances in automated transcription and large language models (LLMs) have automated the transcription and labeling of SRL in these protocols, reducing manual effort. However, while effective in many emerging applications, previous…

This paper demonstrates that a teachable agent (TA) can play a dual role in an online learning environment (OLE) for learning by teaching--the teachable agent working as a synthetic peer for students to learn by teaching and as an interactive tool for cognitive task analysis when authoring an OLE for learning by teaching. We have developed an OLE…

Understanding a student's problem-solving strategy can have a significant impact on effective math learning using Intelligent Tutoring Systems (ITSs) and Adaptive Instructional Systems (AISs). For instance, the ITS/AIS can better personalize itself to correct specific misconceptions that are indicated by incorrect strategies, specific problems can…

In designing an intelligent tutoring system, a core area of the application of AI in education, tips from the system or virtual tutors are crucial in helping students solve difficult questions in disciplines like mathematics. Traditionally, the manual design of general tips by teachers is time-consuming and error-prone. Generative AI, like…

Peer tutoring can improve learning by prompting learners to reflect. To assess whether peer interactions are conducive to learning and provide peer tutoring support accordingly, what tutorial dialog types relate to student learning most? Advancements in collaborative learning analytics allow for merging machine learning-based dialog act…

Twenty-first-century skills should be integrated into higher education to prepare students for complex working-life challenges. Artificial intelligence (AI)-powered tools have the potential to optimise skill development among higher education students. Therefore, it is important to conceptualise relevant affordances of AI systems for 21st-century…

This study used the ARCS approach to investigate the effects of university students' motivation, including attention, relevance, confidence, and satisfaction, to use the Programming Teaching Assistant (PTA) on their Programming Problem-Solving Skills (PPSS). Previous studies have shown that PTA features enhance learners' programming performance,…

In this study, a generative artificial intelligence (AI)-assisted Think-Aloud Pair Problem-Solving (TAPPS) learning strategy was introduced to support ethical dilemma-related problem-solving learning activities. Then, an interactive virtual learning companion system was developed and tested in a business ethics course to evaluate the efficacy of…

Past research has yielded ample knowledge regarding the design of analytics-based tools for teachers and has found beneficial effects of several tools on teaching and learning. Yet there is relatively little knowledge regarding the design of tools that support teachers when a class of students uses AI-based tutoring software for self-paced…

Artificial intelligence (AI) aims to build intelligent systems that can interact with and assist humans. During the interaction, a system learns the requirements from the human user and adapts to the needs to complete tasks. A popular type of interactive system is retrieval-based, where the system uses a retrieval function to retrieve relevant…

This study explores the roles and research foci of AILEd (Artificial Intelligence in Language Education). The AILEd studies published from 1990 to 2020 in the WOS (Web of Science) database were included in the present study. Based on the well-recognized Technology-based Learning Review model, several dimensions, such as research methods, research…