11 Benefits of Using AI Chatbot in the Education Sector

In the supporting learning role (Learning), chatbots are used as an educational tool to teach content or skills. This can be achieved through a fixed integration into the curriculum, such as conversation tasks (L. K. Fryer et al., 2020). Alternatively, learning can be supported through additional offerings alongside classroom teaching, for example, voice assistants for leisure activities at home (Bao, 2019).

Nevertheless, enhancing such skills is often time-consuming, and teachers are usually not mentally prepared to take up a designer’s (Kim, 2021) or programmer’s role. The solution may be situated in developing code-free chatbots (Luo & Gonda, 2019), especially via MIM (Smutny & Schreiberova, 2020). In the mentoring role (Mentoring), chatbot actions deal with the student’s personal development. In this type of support, the student himself is the focus of the conversation and should be encouraged to plan, reflect or assess his progress on a meta-cognitive level. One example is the chatbot in (Cabales, 2019), which helps students develop lifelong learning skills by prompting in-action reflections.

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As Conversational AI and Generative AI continue to advance, chatbots in education will become even more intuitive and interactive. They will play an increasingly vital role in personalized learning, adapting to individual student preferences and learning styles. Moreover, chatbots will foster seamless communication between educators, students, and parents, promoting better engagement and learning outcomes. 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.

One-way user-driven chatbots use machine learning to understand what the user is saying (Dutta, 2017), and the responses are selected from a set of premade answers. In contrast, two-way user-driven chatbots build accurate answers word by word to users (Winkler & Söllner, 2018). Such chatbots can learn from previous user input in similar contexts (De Angeli & Brahnam, 2008). In this approach, the agent acts as a novice and asks students to guide them along a learning route. Rather than directly contributing to the learning process, motivational agents serve as companions to students and encourage positive behavior and learning (Baylor, 2011).

Key Components of Digital Transformation

Finally, Mentoring chatbots to support Learning Skills, in contrast to Self-Regulated Learning, address only particular aspects of the learning process, such as new learning strategies or helpful learning partners. An example for Mentoring chatbots supporting Life Skill is the Logo counseling chatbot, which promotes healthy self-esteem (Engel et al., 2020). CALMsystem is an example of a Self-Regulated Learning chatbot, which informs students about their data in an open learner model (Kerly et al., 2008).

Furthermore, ECs can be operated to answer FAQs automatically, manage online assessments (Colace et al., 2018; Sandoval, 2018), and support peer-to-peer assessment (Pereira et al., 2019). Applying the curve presented in Figure 6 to Gartner’s Hype Cycle (Linden and Fenn, 2003) suggests that technology around chatbots in education may currently be in the “Innovation Trigger” phase. This phase is where many expectations are placed on the technology, but the practical in-depth experience is still largely lacking.

Both Applied Linguistics courses are integral components of the Teacher Education degree programs at the respective universities in Spain and the Czech Republic. These participants were being trained to become English language teachers, and the learning module on chatbot integration into language learning was strategically incorporated into the syllabus of both subjects, taught by the researchers. The choice of Spain and the Czech Republic was primarily based on convenience sampling. The two researchers involved in this study are also lecturers at universities in these respective countries, which facilitated access to a suitable participant pool.

Institutional staff, especially teachers, are often overburdened and exhausted, working beyond their office hours just to deliver excellent learning experiences to their students. Repetitive tasks can easily be carried out using chatbots as teachers’ assistants. With artificial intelligence, chatbots can assist teachers in justifying their work without exhausting them too much. This, in turn, allows teachers to devote more time and attention to designing exciting lessons and providing learners with the personalized attention they deserve. When you think of advancements in technology, edtech might not be the first thing that pops into your head.

SYSTEMATIC REVIEW article

In 2023, AI chatbots are transforming the education industry with their versatile applications. Among the numerous use cases of chatbots, there are several industry-specific applications of AI chatbots in education. Institutions seeking support in any of these areas can implement chatbots and anticipate remarkable benefits of chatbots in education outcomes. Chatbots serve as valuable assistants, optimizing resource allocation in educational institutions. By efficiently handling repetitive tasks, they liberate valuable time for teachers and staff. As a result, schools can reduce the need for additional support staff, leading to cost savings.

These educational chatbots are like magical helpers transforming the way schools interact with students. Now we can easily explore all kinds of activities related to our studies, thanks to these friendly AI companions by our side. In our review process, we carefully adhered to the inclusion and exclusion criteria specified in Table 2.

Purpose of the study

Moreover, chatbots may interact with students individually (Hobert & Meyer von Wolff, 2019) or support collaborative learning activities (Chaudhuri et al., 2009; Tegos et al., 2014; Kumar & Rose, 2010; Stahl, 2006; Walker et al., 2011). Chatbot interaction is achieved by applying text, speech, graphics, haptics, gestures, and other modes of communication to assist learners in performing educational tasks. In our study, the term ‘perceptions’ is defined, following Chuah and Kabilan’s approach (2021), as users’ attitudes and opinions towards their interactions with chatbots in education. This encompasses aspects such as perceived usefulness, acceptance, and potential interest. Research in this area underscores the importance of understanding users’ viewpoints on chatbots, including their acceptance of these tools in educational settings and their preferences for chatbot-human communication.

• It engaged in text-based conversations and demonstrated the ability to exhibit delusional behavior, offering insights into natural language processing and AI.
• Of these, Skill Improvement is the most popular objective, constituting around one-third of publications (32%).
• For example, Chen et al. (2020) highlighted the effectiveness of AICs for Chinese vocabulary learning by comparing chatbot-based tutoring with traditional classroom settings.
• For instance, some teaching agents recommended tutorials to students based upon learning styles (Redondo-Hernández & Pérez-Marín, 2011), students’ historical learning (Coronado et al., 2018), and pattern matching (Ondáš et al., 2019).

They are characterized by engaging learners in a dialog-based conversation using AI (Gulz et al., 2011). The design of CPAs must consider social, emotional, cognitive, and pedagogical aspects (Gulz et al., 2011; King, 2002). The CHISM model offers a comprehensive approach to evaluating AICs, encompassing not only linguistic capabilities but also design and user experience aspects. This holistic evaluation allows for a more nuanced understanding of the strengths and weaknesses of AICs, providing valuable insights for future improvements. The model also highlights the potential of AICs in language learning, particularly in terms of providing immediate feedback, and fostering a supportive learning environment.

Use Case 1: Skills Sandbox

In terms of the evaluation methods used to establish the validity of the articles, two related studies (Pérez et al., 2020; Smutny & Schreiberova, 2020) discussed the evaluation methods in some detail. However, this study contributes more comprehensive evaluation details such as the number of participants, statistical values, findings, etc. Suggestions, stories, and resources come from conversations with students and instructors based on their experience, as well as from external research. Specific sources listed are only for reference and will evolve with the evidence base.

After coding a larger set of publications, it became clear that the code for service-oriented chatbots needed to be further distinguished. This was because it summarized e.g. automation activities with activities related to self-regulated learning and thus could not be distinguished sharply enough from the learning role. After refining the code set in the next iteration into a learning role, an assistance role, and a mentoring role, it was then possible to ensure the separation of the individual codes. To delineate and map the field of chatbots in education, initial findings were collected by a preliminary literature search.