# Using Educational Chatbots with Metacognitive Feedback to Improve Science Learning
## 1 Introduction  
In recent years, artificial intelligence (AI) has been increasingly integrated into the educational field, providing novel solutions to enhance learning and teaching. AI plays a variety of roles, such as intelligent tutors, collaborative learning partners, valuable tools, and even advisers on education policy [1]. Advances in AI and natural language processing have led to the development of educational chatbots as intelligent tutors in the field of education. Educational chatbots (ECs) can simulate human conversation and interact with students synchronously, allowing students to engage actively and take control of their learning. The human-EC interaction can happen via text and voice [2] or the usage of buttons [3]. ECs are usually designed to provide learning content or instant instructional feedback [4]. Hence, they can complement traditional educational settings, offering personalized learning experiences. Science education involves many complex concepts that are challenging for students to memorize and comprehend. The development of learners' conceptual understandingshas long been a central focus of science education research [5]. Interactive technologies may help students better understand complex content [6]. Hence, by using the interactive ECs, students can receive, anytime and anywhere, interaction and instant learning support [7; 8]; therefore, they may better understand science concepts. There is a growing application of ECs in science education. Studies show that ECs enhanced the learning experience in science classrooms [9; 10]. However, there is a lack of research on developing students' metacognitive skills through ECs to promote learning performance and intrinsic motivation. Metacognition is often described as \"thinking about one's own thinking\" [11] and \"monitoring and controlling one's mental processing\" [12]. Metacognition has been shown to lead to deeper, more durable, and more transferable learning [13]. Students with metacognitive skills can recognize their learning abilities as they engage in learning. These skills empower them to effectively plan, monitor, regulate, and assess their learning journey. Developing metacognitive skills will help students remember and apply their acquired knowledge to new situations [14; 15]. While the positive role of metacognition in science teaching and learning are widely recognized [5], determining how to integrate the EC with metacognitive feedback to improve science learning performance and motivation remains unknown. In addition, most theories of self-regulated learning hypothesize that metacognition and motivation interact to determine learning outcomes [16; 17; 18]. However, there is less work examining metacognitive, intrinsic motivational, and learning performance in a single study. According to the self-determination theory, students with higher intrinsic motivation pay more attention to learning and produce high-quality learning outcomes [19]. For example, the research by [PERSON] and [PERSON] found that students who reported higher intrinsic motivation achieved better accuracy on knowledge transfer tests [20]. Furthermore, research revealed that metacognition simultaneously affected the level of intrinsic motivation while intrinsic motivation influenced the quality of metacognition [21]. Hence, intrinsic motivation may play a mediating role in metacognition improving knowledge transfer. Considering the aforementioned issues, this study aims to develop an EC-based dynamic assessment model integrated with metacognitive feedback (ECDA-MF model). The model aims to guide students in studying biology science and to assess students' knowledge retention, transfer, and intrinsic motivation. The rest of this paper is organized as follows: Section 2 reviews the related work and presents research questions and hypotheses. Section 3 describes the the design of learning model and the EC, and explains the experimental procedure. Section 4 presents the results obtained. Section 5 discusses the results and implications for future research.