Learning bots: chatbot for education

This study focuses on using chatbots as a learning assistant from an educational perspective by comparing the educational implications with a traditional classroom. Therefore, the outcomes of this study reflected only on the pedagogical outcomes intended for design education and project-based learning and not the interaction behaviors. As users, the students may have different or higher expectations of EC, which are potentially a spillover from use behavior from chatbots from different service industries. Moreover, questions to ponder are the ethical implication of using EC, especially out of the learning scheduled time, and if such practices are welcomed, warranted, and accepted by today’s learner as a much-needed learning strategy.

Asian universities have contributed 10 articles, while American universities contributed 9 articles. Finally, universities from Africa and Australia contributed 4 articles (2 articles each). According to their relevance to our research questions, we evaluated the found articles using the inclusion and exclusion criteria provided in Table 3.

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The pandemic really forced the education industry to update its teaching style and the results it generated changed the distance learning game completely. Online education is no longer restricted to mere online certification courses on platforms like coursera and udemy anymore. Universities offer distance learning programs, online flagship courses and much more. With edtech companies at its core, chatbot for education has become a new norm and made life easier for students, professors and even the administration department.

• According to Kumar et al. (2021), collaborative learning has a symbiotic relationship with communication skills in project-based learning.
• This study report theoretical and practical contributions in the area of educational chatbots.
• Rule-Based Chatbots Operating based on predefined rules and limited responses.
• While many different chatbots and LLMs exist, we choose to highlight four prominent chatbots currently available for free.
• Five articles (13.88%) presented desktop-based chatbots, which were utilized for various purposes.

As a digital assistant, the EC was designed to aid in managing the team-based project where it was intended to communicate with students to inquire about challenges and provide support and guidance in completing their tasks. According to Cunningham-Nelson et al. (2019), such a role improves academic performance as students prioritize such needs. Therefore, supporting the outcome of this study that observed that the EC groups learning performance and teamwork outcome had a more significant effect size than the CT group. Subsequently, the chatbot named after the course code (QMT212) was designed as a teaching assistant for an instructional design course. It was targeted to be used as a task-oriented (Yin et al., 2021), content curating, and long-term EC (10 weeks) (Følstad et al., 2019). Students worked in a group of five during the ten weeks, and the ECs’ interactions were diversified to aid teamwork activities used to register group members, information sharing, progress monitoring, and peer-to-peer feedback.

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It excels at capturing and retaining contextual information throughout interactions, leading to more coherent and contextually relevant conversations. Unlike some educational chatbots that follow predetermined paths or rely on predefined scripts, ChatGPT is capable of engaging in open-ended dialogue and adapting to various user inputs. This study report theoretical and practical contributions in the area of educational chatbots. Firstly, given the novelty of chatbots in educational research, this study enriched the current body of knowledge and literature in EC design characteristics and impact on learning outcomes.

Subsequently, motivational beliefs are reflected by perceived self-efficacy and intrinsic values students have towards their cognitive engagement and academic performance (Pintrich & de Groot, 1990). According to Pintrich et al. (1993), self-efficacy and intrinsic value strongly correlate with task value (Eccles & Wigfield, 2002), such as interest, enjoyment, and usefulness. Ensuing, the researcher also considered creative self-efficacy, defined as the students’ belief in producing creative outcomes (Brockhus et al., 2014). Prior research has not mentioned creativity as a learning outcome in EC studies. However, according to Pan et al. (2020), there is a positive relationship between creativity and the need for cognition as it also reflects individual innovation behavior. Likewise, it was deemed necessary due to the nature of the project, which involves design.

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Algorithms allow chatbots to learn, intuiting the habits and understanding the tastes and preferences of users. This study applies an interventional study using a quasi-experimental design approach. Creswell (2012) explained that education-based research in most cases requires intact groups, and thus creating artificial groups may disrupt classroom learning. Therefore, one group pretest–posttest design was applied for both groups in measuring learning outcomes, except for learning performance and perception of learning which only used the post-test design. The EC is usually deployed for the treatment class one day before the class except for EC6 and EC10, which were deployed during the class. Such a strategy was used to ensure that the instructor could guide the students the next day if there were any issues.

Subsequently, we delve into the methodology, encompassing aspects such as research questions, the search process, inclusion and exclusion criteria, as well as the data extraction strategy. Moving on, we present a comprehensive analysis of the results in the subsequent section. Finally, we conclude by addressing the limitations encountered during the study and offering insights into potential future research directions.

Student Engagement

AI chatbots can help scenarios, role-playing a situation, and providing feedback. For example, you might prompt the chatbot to create a realistic ethical dilemma that applies to the discipline or to role-play as a patient or client in a relevant scenario. Also, imagine the usefulness of chatbots in times of crises, when parents and students have loads of questions that can overwhelm school employees. Such is the case of the 2020 pandemic when schools may slowly reopen and many parents are concerned about the dangers. As students get back to the classroom, questions about health and safety measures, school hours, and protective gear are likely to rise in numbers. As schools across the country debate banning AI chatbots, some math and computer science teachers are embracing them as just another tool.

Secondly, understanding how different student characteristics interact with chatbot technology can help tailor educational interventions to individual needs, potentially optimizing the learning experience. Thirdly, exploring the specific pedagogical strategies employed by chatbots to enhance learning components can inform the development of more effective educational tools and methods. Nevertheless, Wang et al. (2021) claims while the application of chatbots in education are novel, it is also impacted by scarcity. Nevertheless, while this absence is inevitable, it also provides a potential for exploring innovations in educational technology across disciplines (Wang et al., 2021). Furthermore, according to Tegos et al. (2020), investigation on integration and application of chatbots is still warranted in the real-world educational settings.

Enhanced student engagement through chatbot interactions

Drawing from extensive systematic literature reviews, as summarized in Table 1, AI chatbots possess the potential to profoundly influence diverse aspects of education. They contribute to advancements in both teaching and learning processes. 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. Like any technology, access to these tools varies and lack of access can perpetuate existing inequities.

In the US alone, the chatbot industry was valued at 113 million US dollars and is expected to reach 994.5 million US dollars in 2024 Footnote 1. A chatbot in the education industry is an AI-powered virtual assistant designed to interact with students, teachers, and other stakeholders in the educational ecosystem. Using advanced Conversational AI and Generative AI technologies, chatbots can engage in natural language conversations, providing personalized support and delivering relevant information on various educational topics. Chatbots in education offer unparalleled accessibility, functioning as reliable virtual assistants that remain accessible around the clock.

Exploring the pedagogical uses of AI chatbots

You can use generative AI chatbots to support teaching and learning in many ways. Here we will guide you through exploring various use cases and examples. We also encourage you to access and use chatbots to complete some provided sample tasks. Juji chatbots can also read between the lines to truly understand each student as a unique individual.

Nevertheless, given the possibilities of MIM in conceptualizing an ideal learning environment, we often overlook if instructors are capable of engaging in high-demand learning activities, especially around the clock (Kumar & Silva, 2020). Chatbots can potentially be a solution to such a barrier (Schmulian & Coetzee, 2019), especially by automatically supporting learning communication and interactions (Eeuwen, 2017; Garcia Brustenga et al., 2018) for even a large number of students. Most researchers (25 articles; 69.44%) developed chatbots that operate on the web (Fig. 5).

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