Benefits and Barriers of Chatbot Use in Education Technology and the Curriculum: Summer 2023

Firstly, they can collect and analyze data to offer rich insights into student behavior and performance to help them create more effective learning programs. Secondly, chatbots can gather data on student interactions, feedback, and performance, which can be used to identify areas for improvement and optimize learning outcomes. Thirdly education chatbots can access examination data and student responses in order to perform automated assessments. The bots can then process this information on the instructor’s request to generate student-specific scorecards and provide learning gap insights.

Elements such as the chatbot interface and multimedia content hold substantial importance in this regard. An intuitive and user-friendly interface enriches the overall user experience and encourages interaction (Chocarro et al., 2021; Yang, 2022). Additionally, the incorporation of engaging multimedia content, including videos, images, and other emerging technologies, can also increase users’ attention and engagement (Jang et al., 2021; Kim et al., 2019). Some studies have emphasized that interactions with AICs can seem detached and lack the human element (Rapp et al., 2021). Additionally, while AICs can handle a wide range of queries, they may struggle with complex language nuances, which could potentially lead to misunderstandings or incorrect language usage.

These features include the ability to customize avatars (age, gender, voice, etc.) similar to intelligent conversational agents such as Replika. For example, incorporating familiar characters from cartoons or video games into chatbots can enhance engagement, particularly for children who are learning English by interacting with their favorite characters. Furthermore, by incorporating Augmented Reality (AR) technology, avatars can be launched and video calls can be enabled on social platforms such as Kuki.ai, thereby adding a layer of personal interaction. Looking ahead, allowing students to select specific design aspects of AICs, similar to choosing linguistic features such as target level or accent, could be a crucial step in creating a more adaptive and personalized learning experience. It is evident that chatbot technology has a significant impact on overall learning outcomes.

Participants and context

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. In this paper, we investigated the state-of-the-art of chatbots in education according to five research questions.

• In 2023, AI chatbots are transforming the education industry with their versatile applications.
• All three authors collaborated on the selection of the final paper collection and contributed to crafting the conclusion.
• A systematic review follows a rigorous methodology, including predefined search criteria and systematic screening processes, to ensure the inclusion of relevant studies.

ELIZA could mimic human-like responses by reflecting user inputs as questions. Another early example of a chatbot was PARRY, implemented in 1972 by psychiatrist Kenneth Colby at Stanford University (Colby, 1981). PARRY was a chatbot designed to simulate a paranoid patient with schizophrenia. It engaged in text-based conversations and demonstrated the ability to exhibit delusional behavior, offering insights into natural language processing and AI.

The adoption of educational chatbots is on the rise due to their ability to provide a cost-effective method to engage students and provide a personalized learning experience (Benotti et al., 2018). Chatbot adoption is especially crucial in online classes that include many students where individual support from educators to students is challenging (Winkler & Söllner, 2018). 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. From the viewpoint of educators, integrating AI chatbots in education brings significant advantages.

3 RQ3 – What role do the educational chatbots play when interacting with students?

Considering Microsoft’s extensive integration efforts of ChatGPT into its products (Rudolph et al., 2023; Warren, 2023), it is likely that ChatGPT will become widespread soon. Educational institutions may need to rapidly adapt their policies and practices to guide and support students in using educational chatbots safely and constructively manner (Baidoo-Anu & Owusu Ansah, 2023). Educators and researchers must continue to explore the potential benefits and limitations of this technology to fully realize its potential. This gap is more pronounced in understanding how the design and linguistic features of AICs impact user satisfaction and engagement.

Nonetheless, certain researchers, including Ayedoun et al. (2015) and Fryer et al. (2019), have indicated that the initial enthusiasm and engagement students show towards chatbots may be short-lived, attributing this to the novelty effect of this technology. PU is the belief that a particular technological system will be beneficial if adopted, such that the more useful a technology is perceived, the more likely it will be used (Davis et al., 1989). PU has been identified in the literature as a factor determining whether teachers and students adopt chatbots (Chocarro et al., 2021; Malik et al., 2021; Mohd Rahim et al., 2022). The usefulness of AI in education is unfamiliar to some teachers (Hrastinski et al., 2019), and many have had negative experiences using chatbots (Kim & Kim, 2022).

It has also been observed that some students’ interest dwindled after the initial period of engagement due to repetitive conversation patterns and redundancies, making the interaction less natural compared to student–teacher exchanges (Fryer et al., 2019). A chatbot, short for chatterbot, is a computer program that uses artificial intelligence (AI) to conduct a conversation via auditory or textual methods and interacts with humans in their natural languages. These interactions usually occur through websites, messaging applications, or mobile apps, where the bot is capable of simulating and maintaining human-like conversations and perform different tasks (Adamopoulou & Moussiades, 2020). In addition, the responses of the learner not only determine the chatbot’s responses, but provide data for the teacher to get to know the learner better.

Later in 2001 ActiveBuddy, Inc. developed the chatbot SmarterChild that operated on instant messaging platforms such as AOL Instant Messenger and MSN Messenger (Hoffer et al., 2001). SmarterChild was a chatbot that could carry on conversations with users about a variety of topics. It was also able to learn from its interactions with users, which made it more and more sophisticated over time. In 2011 Apple introduced Siri as a voice-activated personal assistant for its iPhone (Aron, 2011). You can foun additiona information about ai customer service and artificial intelligence and NLP. 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).

By analyzing conversation data, educational institutions can gain insights into user preferences, pain points, and popular inquiries, informing decision-making and strategy. In the fast-paced educational environment, providing instant assistance is crucial. Chatbots excel at offering immediate support on a 24/7 basis, helping students with queries, and directing https://chat.openai.com/ them to the appropriate resources. The collection of information is necessary for chatbots to function, and the risks involved with using chatbots need to be clearly outlined for teachers. Informed consent in plain language should be addressed prior to the use of chatbots and is currently a concern for the Canadian government (CBC News, 2023).

Instead of enduring the hassle of visiting the office and waiting in long queues for answers, students can simply text the chatbots to quickly resolve their queries. This user-friendly option provides convenient and efficient access to information, enhancing the overall student experience and streamlining administrative processes. Whether it’s admission-related inquiries or general questions, educational chatbots offer a seamless and time-saving alternative, empowering students with instant and accurate assistance at their fingertips. 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.

Winkler and Söllner (2018) reviewed 80 articles to analyze recent trends in educational chatbots. The authors found that chatbots are used for health and well-being advocacy, language learning, and self-advocacy. Chatbots are either flow-based or powered by AI, concerning approaches to their designs.

You can picture it as a sidekick in your pocket, one that has been trained at the d.school, has “learned” a large number of design methods, and is always available to offer its knowledge to you. I do not see chatbots as a replacement for the teacher, but as one more tool in their toolbox, or a new medium that can be used to design learning experiences in a way that extends the capacity and unique abilities of the teacher. When using a chatbot, the gathering of data and feedback from the students happens in a way that is organic and integrated into the learning benefits of chatbots in education experience — without the need for separate surveys or tests. The data is captured digitally in a format that can be analyzed manually or by using algorithms that can detect themes, patterns, and connections. In effect the teacher can “interact” with and learn from multiple learners at the same time (in theory an infinite number of them). 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.

The integration of artificial intelligence (AI) chatbots in education has the potential to revolutionize how students learn and interact with information. One significant advantage of AI chatbots in education is their ability to provide personalized and engaging learning experiences. By tailoring their interactions to individual students’ needs and preferences, chatbots offer customized feedback and instructional support, ultimately enhancing student engagement and information retention. However, there are potential difficulties in fully replicating the human educator experience with chatbots. While they can provide customized instruction, chatbots may not match human instructors’ emotional support and mentorship.

Chatbots can help foster a sense of community among online learners by connecting them with peers, facilitating group discussions, and providing support for collaborative projects. Thus, the chatbot ensures that all potential students receive prompt and accurate information without overwhelming the support staff. Chatbots can easily scale to handle increased demand, managing thousands of conversations without compromising support quality. This can help online schools accommodate rapid growth or seasonal fluctuations in user inquiries. In the assisting role (Assisting), chatbot actions can be summarized as simplifying the student’s everyday life, i.e., taking tasks off the student’s hands in whole or in part. This can be achieved by making information more easily available (Sugondo and Bahana, 2019) or by simplifying processes through the chatbot’s automation (Suwannatee and Suwanyangyuen, 2019).

The development of LLM-power chatbots could help avoid irrelevant responses often resulting from an over-reliance on pre-set answers, as indicated by Jeon (2021). Qualitative data were collected through class discussions and assessment reports of the AICS following a template provided through the Moodle platform. During the 1-month intervention period in each educational setting, participants independently completed the assessment reports.

Thirdly, exploring the specific pedagogical strategies employed by chatbots to enhance learning components can inform the development of more effective educational tools and methods. Artificial Intelligence (AI) technologies have increasingly become vital in our everyday lives. Education is one of the most visible domains in which these technologies are being used. Conversational Agents (CAs) are among the most prominent AI systems for assisting teaching and learning processes.

Adeel Akram, Senior Account Executive for respond.io, highlights the prominent use cases he encountered in the education field. PEU is the degree to which an individual feels like a technology is easy to use (Davis et al., 1989). As PEU increases, the intention to use chatbots by teachers and administrators (Pillai et al., 2023) and post-graduate students increases (Mohd Rahim et al., 2022). In addition, the students surveyed by Mohd Rahim et al. (2022) indicated that if chatbots increased the PEU of other tasks, they would be more inclined to adopt the technology. The following recommendations for increased technology adoption are based on the current perceptions of chatbots in education. There are multiple business dimensions in the education industry where chatbots are gaining popularity, such as online tutors, student support, teacher’s assistant, administrative tool, assessing and generating results.

Finally, the seventh question discusses the challenges and limitations of the works behind the proposed chatbots and potential solutions to such challenges. 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. This includes activities such as establishing educational objectives, developing teaching methods and curricula, and conducting assessments (Latif et al., 2023).

While studies like those of Chen et al. (2020) and Chocarro et al. (2023) have begun exploring these areas, there is a need for a more targeted framework to evaluate satisfaction with AICs in the context of language learning. To address this need, our study investigates EFL teacher candidates’ levels of satisfaction and perceptions of four AICs. 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. Similarly, ‘satisfaction’ is described as the degree to which users feel that their needs and expectations are met by the chatbot experience, encompassing both linguistic and design aspects.

Moreover, chatbots will foster seamless communication between educators, students, and parents, promoting better engagement and learning outcomes. By harnessing the power of generative AI, chatbots can efficiently handle a multitude of conversations with students simultaneously. The technology’s ability to generate human-like responses in real-time allows these AI chatbots to engage with numerous students without compromising the quality of their interactions. This scalability ensures that every learner receives prompt and personalized support, no matter how many students are using the chatbot at the same time.

Their integration into an e-learning system can provide replies suited to each learner’s specific needs, allowing them to study at their own pace. The related chatbot was implemented and evaluated in Moroccan public schools with the support of teachers from the Regional Center for Education and Training Professions of Souss Massa. One is a control class group that uses a traditional approach, while the other two are experimental groups that employ digital content and the chatbot-based method. Preliminary findings indicate that employing chatbots can greatly enhance student learning experiences by allowing them to study at their own speed with less stress, saving them time, and keeping them motivated. Furthermore, integrating these AI systems into a smart classroom will not only create a supportive environment by encouraging good interactions with students, it will also allow learners to be more engaged and achieve better academic objectives.

They should critically evaluate and fact-check the responses to prevent the spread of misinformation or disinformation. Chatbots’ responses can vary in accuracy, and there is a risk of conveying incorrect or biased information. Universities must ensure quality control mechanisms to verify the accuracy and reliability of the AI-generated content. 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. The advantages and challenges of using chatbots in universities share similarities with those in primary and secondary schools, but there are some additional factors to consider, discussed below.

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. Other authors, such as (Daud et al., 2020), used a slightly different approach where the chatbot guides the learners to select the topic they would like to learn.

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. The American Council on Science and Health is a research and education organization operating under Section 501(c)(3) of the Internal Revenue Code. Ethical issues such as bias, fairness, and privacy are relevant in university settings. Universities should address these concerns and establish ethical guidelines for the responsible use of AI technologies. “I also gave it the challenge of coming up with creative ideas for foods in my fridge based on an original photo (it identified the items correctly, though the creative recipe suggestions were mildly horrifying).”

Understanding student sentiments during and after the sessions is very important for teachers. If students end up being confused and unclear about the topic, all the efforts made by the teachers go in vain. With artificial intelligence, the complete process of enrollment and admissions can be smoother and more streamlined.

Search Criteria

His research focuses on public policy toward science, technology, and medicine, encompassing a number of areas, including pharmaceutical development, genetic engineering, models for regulatory reform, precision medicine, and the emergence of new viral diseases. Dr. Miller served for fifteen years at the US Food and Drug Administration (FDA) in a number of posts, including as the founding director of the Office of Biotechnology. However, like most powerful technologies, the use of chatbots offers challenges and opportunities. Users should prioritize the privacy and data protection of individuals when using chatbots.

Concerning RQ2 (pedagogical roles), our results show that chatbots’ pedagogical roles can be summarized as Learning, Assisting, and Mentoring. The Learning role is the support in learning or teaching activities such as gaining knowledge. The Assisting role is the support in terms of simplifying learners’ everyday life, e.g. by providing opening times of the library. The Mentoring role is the support in terms of students’ personal development, e.g. by supporting Self-Regulated Learning. From a pedagogical standpoint, all three roles are essential for learners and should therefore be incorporated in chatbots. These pedagogical roles are well aligned with the four implementation objectives reported in RQ1.

Building a Chatbot for Education: Tips and Tricks

From one day to the next, instructors had to figure out how to teach in a distributed and chimeric space, in which their home office — or kitchen, or living room — was connected to the many home spaces (or coffee shops) where the students could find access to Wi-Fi. 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. Various design principles, including pedagogical ones, have been used in the selected studies (Table 8, Fig. 8). Concerning the platform, chatbots can be deployed via messaging apps such as Telegram, Facebook Messenger, and Slack (Car et al., 2020), standalone web or phone applications, or integrated into smart devices such as television sets. Henry I. Miller, MS, MD, is the Glenn Swogger Distinguished Fellow at the American Council on Science and Health.

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). 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). When the user provides answers compatible with the flow, the interaction feels smooth.

• Researchers are strongly encouraged to fill the identified research gaps through rigorous studies that delve deeper into the impact of chatbots on education.
• The findings point to improved learning, high usefulness, and subjective satisfaction.
• The researchers recorded the facial expressions of the participants using webcams.
• As a result, schools can reduce the need for additional support staff, leading to cost savings.
• 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.
• I’m also very clear, through what the bot says to the user and what I say when I first introduce the bot, about how the information that is shared will be used.

By far, the majority (20; 55.55%) of the presented chatbots play the role of a teaching agent, while 13 studies (36.11%) discussed chatbots that are peer agents. Only two studies used chatbots as teachable agents, and two studies used them as motivational agents. In comparison, chatbots used to teach languages received less attention from the community (6 articles; 16.66%;). Interestingly, researchers used a variety of interactive media such as voice (Ayedoun et al., 2017; Ruan et al., 2021), video (Griol et al., 2014), and speech recognition (Ayedoun et al., 2017; Ruan et al., 2019).

In addition, some researchers are concerned about the spread of misinformation from the text produced by chatbots (Hsu & Thompson, 2023, February 8). ChatGPT is widely considered to be the highest quality chatbot currently available and is only accurate approximately 60% of the time when tested with OpenAI’s internal testing and TruthfulQA’s external benchmarking (OpenAI, 2023a). Facilitating conditions refer to the degree to which an individual believes that there will be technological support from their system or organization (Chan et al., 2010).

The comprehensive list of included studies, along with relevant data extracted from these studies, is available from the corresponding author upon request. 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. We recommend using respond.io, an AI-powered customer conversation management software. You can start with a free trial and later upgrade to the plan that best suits your business needs. “With many institutions offering similar programs, such as the numerous universities in Malaysia presenting executive MBAs (Master of Business Administration), acquiring customers becomes a challenge.

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).

Chatbots will be virtual assistants that offer instant help and answer questions whenever students get stuck understanding a concept. 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.

The chatbots studied in the current literature are traditional, FAQ-type chatbots. 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.

Conversely, OpenAI restricts access to ChatGPT in certain countries, such as Afghanistan and Iran, citing geopolitical constraints, legal considerations, data protection regulations, and internet accessibility as the basis for this decision. Italy became the first Western country to ban ChatGPT (Browne, 2023) after the country’s data protection authority called on OpenAI to stop processing Italian residents’ data. They claimed that ChatGPT did not comply with the European General Data Protection Regulation. However, after OpenAI clarified the data privacy issues with Italian data protection authority, ChatGPT returned to Italy. To avoid cheating on school homework and assignments, ChatGPT was also blocked in all New York school devices and networks so that students and teachers could no longer access it (Elsen-Rooney, 2023; Li et al., 2023).

For instance, Okonkwo and Ade-Ibijola (2021) found out that chatbots motivate students, keep them engaged, and grant them immediate assistance, particularly online. Additionally, Wollny et al. (2021) argued that educational chatbots make education more available and easily accessible. Additionally, speech technologies emerged as an area requiring substantial improvement, in line with previous results (Jeon et al., 2023). With the exception of Buddy.ai, the voice-based interactions provided very low results due to poor speech recognition Chat PG and dissatisfaction with the synthesized voice, potentially leading to student anxiety and disengagement. Simultaneously, rendering the AICs’ voice generation more human-like can be attained through more sophisticated Text-to-Speech (TTS) systems that mimic the intonation, rhythm, and stress of natural speech (Jeon et al., 2023). For the interaction, detailed instructions were provided via Moodle, with the aim not to measure the participants’ English learning progress, but to enable critical analysis of each AIC as future educators.