Interacting with educational chatbots: A systematic review Education and Information Technologies

In 2011 Apple introduced Siri as a voice-activated personal assistant for its iPhone (Aron, 2011). Although not strictly a chatbot, Siri showcased the potential of conversational AI by understanding and responding to voice commands, performing tasks, and providing information. In the same year, IBM’s Watson gained fame by defeating human champions in the quiz show Jeopardy (Lally & Fodor, 2011). It demonstrated the power of natural language processing and machine learning algorithms in understanding complex questions and providing accurate answers. More recently, in 2016, Facebook opened its Messenger platform for chatbot development, allowing businesses to create AI-powered conversational agents to interact with users.

• Moreover, it has been found that teaching agents use various techniques to engage students.
• This constant accessibility allows learners to seek support, access resources, and engage in activities at their convenience.
• We raise our funds each year primarily from individuals and foundations.
• Smutny and Schreiberova (2020) examined chatbots as a learning aid for Facebook Messenger.
• In recent years, chatbots have emerged as powerful tools in various industries, including education.

Further, we excluded tutorials, technical reports, posters, and Ph.D. thesis since they are not peer-reviewed. Only a few studies partially tackled the principles guiding the design of the chatbots. For instance, Martha and Santoso (2019) discussed one aspect of the design (the chatbot’s visual appearance). This study focuses on the conceptual principles that led to the chatbot’s design. Conversational AI is revolutionizing the way businesses communicate with their customers and everyone is loving this new way.

What do I need to build a chatbot?

The study by Pérez et al. (2020) reviewed the existing types of educational chatbots and the learning results expected from them. Smutny and Schreiberova (2020) examined chatbots as a learning aid for Facebook Messenger. Okonkwo and Ade-Ibijola (2021) analyzed the main benefits and challenges of implementing chatbots in an educational setting.

Thereby, chatbots assist in building and nurturing a robust grads network. Such a contribution also offers networking opportunities and support for current students. You can foun additiona information about ai customer service and artificial intelligence and NLP. Additionally, this will positively impact the brand image, attracting potential applicants and stakeholders. During holiday periods, when learners might face difficulties reaching teachers, chatbots become valuable tools for assistance. They facilitate communication of homework details, schedules, and answer queries.

She recently has developed the “d.bot,” which takes a software feature that many of us know through our experiences as customers — the chatbot — and deploys it instead as a tool for teaching and learning. Jenny Robinson, a member of the Stanford Digital Education team, discussed with Britos Cavagnaro what led to her innovation, how it’s working and what she sees as its future. AI chatbots can be attentive to – and train on – students’ learning habits and areas of difficulty.

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 outcomes. The Explain My Answer option provides learners with an opportunity to delve deeper into their responses. By selecting a button following specific exercise types, users engage in a chat with Duo, receiving a concise explanation about their answers.

Cognitive AI for Education

Only four studies (Hwang & Chang, 2021; Wollny et al., 2021; Smutny & Schreiberova, 2020; Winkler & Söllner, 2018) examined the field of application. None of the studies discussed the platforms on which the chatbots run, while only one study (Wollny et al., 2021) analyzed the educational roles the chatbots are playing. The study used “teaching,” “assisting,” and “mentoring” as categories for educational roles. This study, however, uses different classifications (e.g., “teaching agent”, “peer agent”, “motivational agent”) supported by the literature in Chhibber and Law (2019), Baylor (2011), and Kerlyl et al. (2006). Other studies such as (Okonkwo and Ade-Ibijola, 2021; Pérez et al., 2020) partially covered this dimension by mentioning that chatbots can be teaching or service-oriented. Winkler and Söllner (2018) reviewed 80 articles to analyze recent trends in educational chatbots.

For instance, Winkler and Söllner (2018) classified the chatbots as flow or AI-based, while Cunningham-Nelson et al. (2019) categorized the chatbots as machine-learning-based or dataset-based. In this study, we carefully look at the interaction style in terms of who is in control of the conversation, i.e., the chatbot or the user. As such, we classify the interactions as either chatbot or user-driven.

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. Through interactive conversations, thought-provoking questions, and the delivery of intriguing information, chatbots in education captivate students’ attention, making learning an exciting and rewarding adventure. By creating a sense of connection and personalized interaction, these AI chatbots forge stronger bonds between students and their studies. Learners feel more immersed and invested in their educational journey, driven by the desire to explore new topics and uncover intriguing insights. Additionally, tutoring chatbots provide personalized learning experiences, attracting more applicants to educational institutions.

The surveyed articles used different types of empirical evaluation to assess the effectiveness of chatbots in educational settings. In some instances, researchers combined multiple Chat PG evaluation methods, possibly to strengthen the findings. Six (16.66%) articles presented educational chatbots that exclusively operate on a mobile platform (e.g., phone, tablet).

Challenges in chatbot development include insufficient training datasets, a lack of emphasis on usability heuristics, ethical concerns, evaluation methods, user attitudes, programming complexities, and data integration issues. Only one study pointed to high usefulness and subjective satisfaction (Lee et al., 2020), while the others reported low to moderate subjective satisfaction (Table 13). For instance, the chatbot presented in (Lee et al., 2020) aims to increase learning effectiveness by allowing students to ask questions related to the course materials.

The COVID-19 pandemic pushed educators and students out of their classrooms en masse. It was a great opportunity to be creative and figure out how to activate in-context learning, taking advantage of the unique spaces where the students were, and the wide world out there. In conversations with other people, we routinely ask for clarifying details, repeat ideas in different ways, allow a conversation to go in unexpected directions, and guide others back to the topic at hand.

Teaching agents play the role of human teachers and can present instructions, illustrate examples, ask questions (Wambsganss et al., 2020), and provide immediate feedback (Kulik & Fletcher, 2016). On the other hand, peer agents serve as learning mates for students to encourage peer-to-peer interactions. The agent of this approach is less knowledgeable than the teaching agent. Nevertheless, peer agents can still guide the students along a learning path. Students typically initiate the conversation with peer agents to look up certain definitions or ask for an explanation of a specific topic.

Juji chatbots can also read between the lines to truly understand each student as a unique individual. This enables Juji chatbots to serve as a student’s personal learning assistant or an instructor’s teaching assistant, to personalize teaching and optimize learning outcomes. In the form of chatbots, Juji cognitive AI assistants automate high-touch student engagements empathetically. Most learning happens in the 99.9% of our lives when we are not in a classroom.

Frequently Asked Questions about Chatbot for Education

These so-called “chatbots,” computer programs designed to simulate conversation with human users, have evolved rapidly in recent years. There is also a bias towards empirically evaluated articles as we only selected articles that have an empirical evaluation, such as experiments, evaluation studies, etc. Further, we only analyzed the most recent articles when many articles discussed the same concept by the same researchers. The results show that the chatbots were proposed in various areas, including mainly computer science, language, general education, and a few other fields such as engineering and mathematics. Most chatbots are accessible via a web platform, and a fewer chatbots were available on mobile and desktop platforms. This choice can be explained by the flexibility the web platform offers as it potentially supports multiple devices, including laptops, mobile phones, etc.

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. Notable examples are explained in (Rodrigo et al., 2012; Griol et al., 2014), where the authors presented a chatbot that asks students questions and provides them with options to choose from.

AI systems may lack the emotional understanding and sensitivity required for dealing with complex sentimental concerns. In educational establishments where mental support is essential, the absence of sensitive intelligence in chatbots can limit their effectiveness in addressing users’ personal needs. The widespread adoption of chatbots and their increasing accessibility has sparked contrasting reactions across different sectors, leading to considerable confusion in the field of education.

They also act as study companions, offering explanations and clarifications on various subjects. They can be used for self-quizzing to reinforce knowledge and prepare for exams. Furthermore, these chatbots facilitate flexible personalized learning, tailoring their teaching strategies to suit each student’s unique needs.

However, there have been contradictory findings related to critical thinking, learning engagement, and motivation. Deng and Yu (2023) found that chatbots had a significant and positive influence on numerous learning-related aspects but they do not significantly improve motivation among students. Contrary, Okonkwo and Ade-Ibijola (Okonkwo & Ade-Ibijola, 2021), as well as (Wollny et al., 2021) find that using chatbots increases students’ motivation. Like all of us, teachers are bound by time and space — but can educational technology offer new ways to make a teacher’s presence and knowledge available to learners? Stanford d.school’s Leticia Britos Cavagnaro is pioneering efforts to extend interactive resources beyond the classroom.

Special care must be taken in situations where faulty information could be dangerous, such as in chemistry laboratory experiments, using tools, or constructing mechanical devices or structures. Pérez et al. (2020) identified various technologies used to implement chatbots such as Dialogflow Footnote 4, FreeLing (Padró and Stanilovsky, 2012), and ChatFuel Footnote 5. The study investigated the effect of the technologies used on performance and quality of chatbots. Predicted to experience substantial growth of approximately $9 billion by 2029, the Edtech industry demonstrates numerous practical applications that highlight the capabilities of AI and ML. Chatbots must be designed with strict privacy and security controls to safeguard sensitive information. Visual cues such as progress bars, checkmarks, or typing indicators can help users understand where they are in the conversation and what to expect next.

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. As technology continues to advance, AI-powered educational chatbots are expected to become more sophisticated, providing accurate information and offering even more individualized and engaging learning experiences. They are anticipated to engage with humans using voice recognition, comprehend human emotions, and navigate social interactions. Consequently, their potential impact on future education is substantial. This includes activities such as establishing educational objectives, developing teaching methods and curricula, and conducting assessments (Latif et al., 2023).

Five articles (13.88%) presented desktop-based chatbots, which were utilized for various purposes. For example, one chatbot focused on the students’ learning styles and personality features (Redondo-Hernández & Pérez-Marín, 2011). As another example, the SimStudent chatbot is a teachable agent that students can teach (Matsuda et al., 2013). Only two articles partially addressed the interaction styles of chatbots.

These intelligent assistants are capable of answering queries, providing instant feedback, offering study resources, and guiding educatee through academic content. Besides, institutions can integrate bots into knowledge management systems, websites, or standalone applications. Such solutions lead to efficient learning and administrative supervision. This article sheds light on such tools, exploring their wide-ranging capabilities, limitations, and significant impact on the learning landscape. Read till the end and witness how companies, including Duolingo, leverage innovative technology to make learning accessible to everyone.

This can lead to better performance and enhance the learning experience (Hackman, 2011). For example, teams can use a chatbot to synthesize ideas, develop a timeline of action items, or provide differing perspectives or critiques of the team’s ideas. Remember to take the lead when using chatbots for team projects, making your own choices while incorporating the helpful and discarding what is not. You also want to make sure you are working with an evidence-based platform and that the chatbot is AI-powered and not just a system that can respond with simple answers to simple prompts, Smith says. A robust AI-powered chatbot is able to parse human language and learn from previous conversations to improve accuracy. “A chatbot’s ability to handle multiple languages, to understand run-on questions, handle misspellings, and deal with emojis are all key indicators of a chatbot that is powered by AI,” she says.

Eventually, the tool may include information from other education and tech organizations that ISTE partners with. The Stretch prototype is currently being tested by a select group of people and is not yet available for public use. One of the most popular use cases for chatbots in education is helping with homework.

As an example of an evaluation study, the researchers in (Ruan et al., 2019) assessed students’ reactions and behavior while using ‘BookBuddy,’ a chatbot that helps students read books. The researchers recorded the facial expressions of the participants using webcams. It turned out that the students were engaged more than half of the time while using BookBuddy. Concerning their interaction style, the conversation with chatbots can be chatbot or user-driven (Følstad et al., 2018). Chatbot-driven conversations are scripted and best represented as linear flows with a limited number of branches that rely upon acceptable user answers (Budiu, 2018).

Among them, ChatGPT and Google Bard are among the most profound AI-powered chatbots. It was first announced in November 2022 and is available to the general public. ChatGPT’s rival Google Bard chatbot, developed by Google AI, was first announced in May 2023. Both Google Bard and ChatGPT are sizable language model chatbots that undergo training on extensive datasets of text and code.

Invaluable teaching assistants can give a hand with automation tasks like tests, assessments, and assignment tracking. EdWeek reports that, according to Impact Research, nearly 50% of teachers utilized ChatGPT for lesson planning and generated creative ideas for their classes. LL provided a concise overview of the existing literature and formulated the methodology. All three authors collaborated on the selection of the final paper collection and contributed to crafting the conclusion.

Cases in which conversational AI supports learners and instructional teams

These real-life examples showcase how chatbots are integrated into education and online schools, offering enhanced learning experiences, administrative support, and improved communication. Future studies should explore chatbot localization, where a chatbot is customized based on the culture and context it is used in. Moreover, researchers should explore devising frameworks for designing and developing educational chatbots to guide educators to build usable and effective chatbots.

Among educators and learners, there is a notable trend—while learners are excited about chatbot integration, educators’ perceptions are particularly critical. However, this situation presents a unique opportunity, accompanied by unprecedented challenges. Consequently, it has prompted a significant surge in research, aiming to explore the impact of chatbots on education. With active listening skills, Juji chatbots can help educational organizations engage with their audience (e.g., existing or prospect students) 24×7, answering questions and providing just-in-time assistance. These chatbots are also faster to build and easier to be integrated with other education applications. A well-functioning team can leverage individual team members’ skills, provide social support, and allow for different perspectives.

Another interesting study was the one presented in (Law et al., 2020), where the authors explored how fourth and fifth-grade students interacted with a chatbot to teach it about several topics such as science and history. The students appreciated that the robot was attentive, curious, and eager to learn. Various design principles, including pedagogical ones, have been used in the selected studies (Table 8, Fig. 8). Unsurprisingly, most chatbots were web-based, probably because the web-based applications are operating system independent, do not require downloading, installing, or updating.

SPACE10 (IKEA’s research and design lab) published a fascinating survey asking people what characteristics they would like to see in a virtual AI assistant. Beyond gender and form of the bot, the survey revealed many open questions in https://chat.openai.com/ the growing field of human-robot interaction (HRI). Claude, the name of the large language model and chatbot developed by Anthropic, uses a different method of training from GPT and Bard that aims to focus on safety and helpfulness.

Administrators can take up other complex, time-consuming tasks that need human attention. Automation is essential for all the administrative procedures in schools. Admitting hundreds of students with varied fee structures, course details, and specializations can be a task for administrators.

A user-driven interaction was mainly utilized for chatbots teaching a foreign language. A notable example of a study using questionnaires is ‘Rexy,’ a configurable educational chatbot chatbot for education discussed in (Benedetto & Cremonesi, 2019). The questionnaires elicited feedback from participants and mainly evaluated the effectiveness and usefulness of learning with Rexy.

Chatbots, also known as conversational agents, enable the interaction of humans with computers through natural language, by applying the technology of natural language processing (NLP) (Bradeško & Mladenić, 2012). In fact, the size of the chatbot market worldwide is expected to be 1.23 billion dollars in 2025 (Kaczorowska-Spychalska, 2019). 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. AI implementation promotes higher engagement by supplying interactive learning experiences, making the process more enjoyable. The study shows that 90.7% of participants expressed satisfaction with the experiential learning chatbot workshop, while 81.4% felt engaged. Through tailored interactions, quizzes, and real-time discussions, bots perfectly captivate users’ attention.