Chatbots applications in education: A systematic review
The authors in (Ruan et al., 2021) used a similar approach where students freely speak a foreign language. The chatbot assesses the quality of the transcribed text and provides constructive feedback. In comparison, the authors in (Tegos et al., 2020) rely on a slightly different approach where the students chat together about a specific programming concept. The chatbot intervenes to evoke curiosity or draw students’ attention to an interesting, related idea. 7, most of the articles (88.88%) used the chatbot-driven interaction style where the chatbot controls the conversation. 52.77% of the articles used flow-based chatbots where the user had to follow a specific learning path predetermined by the chatbot.
Then, chatbots use this data to compose an entirely personalized learning program that focuses on troubling subjects. Their job is also to follow the students’ advancement from the first to the last lesson, check their assumptions, and guide them through the curriculum. Some studies mentioned limitations such as inadequate or insufficient dataset training, lack of user-centered design, students losing interest in the chatbot over time, and some distractions.
AI as teammate
Concerning the design principles behind the chatbots, slightly less than a third of the chatbots used personalized learning, which tailored the educational content based on learning weaknesses, style, and needs. Other chatbots used experiential learning (13.88%), social dialog (11.11%), collaborative learning (11.11%), affective learning (5.55%), learning by teaching (5.55%), and scaffolding (2.77%). The surveyed articles used different types of empirical evaluation to assess the effectiveness of chatbots in educational settings. In some instances, researchers combined multiple evaluation methods, possibly to strengthen the findings.
All three authors collaborated on the selection of the final paper collection and contributed to crafting the conclusion. The authors declare that this research paper did not receive any funding from external organizations. The study was conducted independently and without financial support from any source.
Based on a mixed-method quasi-experimental approach, ECs were found to improve learning performance and teamwork with a practical impact. Moreover, it was found that ECs facilitated collaboration among team members that indirectly influenced their ability to perform as a team. Nevertheless, affective-motivational learning outcomes such as perception of learning, need for cognition, motivation, and creative self-efficacy were not influenced by ECs.
The integration of chatbot technology into STEM education, in particular, is transforming how students learn, educators teach, and institutions operate. It is an exciting time for STEM education, with the potential for even more innovations on the horizon. The integration of chatbot technology into STEM education has been a game-changer. Chatbots are AI-powered programs designed to simulate human conversation, providing instant responses to user queries. They are increasingly being used in various educational settings, including schools, colleges, and universities.
Effectiveness of Virtual and Online platforms when teaching STEM & Robotics
Drawing from extensive systematic literature reviews, as summarized in Table 1, AI chatbots possess the potential to profoundly influence diverse aspects of education. However, it is essential to address concerns regarding the irrational use of technology and the challenges that education systems encounter while striving to harness its capacity and make the best use of it. The integration of chatbots into e-learning platforms offers numerous benefits for both students and educators. Firstly, Kearney et al. (2009) explained that in homogenous teams (as investigated in this study), the need for cognition might have a limited amount of influence as both groups are required to be innovative simultaneously in providing project solutions.
Through this encouragement, learners can persevere through setbacks and maintain their drive to succeed. Chatbots can monitor student progress, pinpoint problem areas, and offer constructive criticism to guide students toward mastery. This continuous feedback loop keeps Learners on the proper track and makes consistent progress. With the rapid development of technology today, education has also undergone profound changes. The development of mobile learning chatbots is one of the most ground-breaking innovations in recent years. These smart virtual assistants can facilitate individualized and interactive learning for students and professionals, perhaps ushering in a new era of education and training.
In general, the followed approach with these chatbots is asking the students questions to teach students certain content. Chatbots have been found to play various roles in educational contexts, which can be divided into four roles (teaching agents, peer agents, teachable agents, and peer agents), with varying degrees of success (Table 6, Fig. 6). Exceptionally, a chatbot found in (D’mello & Graesser, 2013) is both a teaching and motivational agent. In general, most desktop-based chatbots were built in or before 2013, probably because desktop-based systems are cumbersome to modern users as they must be downloaded and installed, need frequent updates, and are dependent on operating systems. Unsurprisingly, most chatbots were web-based, probably because the web-based applications are operating system independent, do not require downloading, installing, or updating.
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