Integrating chatbots in education: insights from the Chatbot-Human Interaction Satisfaction Model CHISM Full Text

This is true right from the point of admission and is accomplished by personalizing their learning and gathering important feedback and other data to improve services further. Using chatbots for essay scoring and grading tasks has the potential to revolutionize the educational sector. Intelligent essay-scoring bots can reduce the workload of teachers and provide quicker feedback to students. By reminding students to repeat their learning at spaced intervals, chatbots can help cement the lesson in their minds and improve long-term retention.

You can foun additiona information about ai customer service and artificial intelligence and NLP. The questionnaires used mostly Likert scale closed-ended questions, but a few questionnaires also used open-ended questions. Most articles (13; 36.11%) used an experiment to establish the validity of the used approach, while 10 articles (27.77%) used an evaluation study to validate the usefulness and usability of their approach. The remaining articles used a questionnaire (10; 27.7%) and a focus group (3; 8.22%) as their evaluation methods. 3 is more than 36 (the number of selected articles) as the authors of a single article could work in institutions located in different countries. The vast majority of selected articles were written or co-written by researchers from American universities. However, the research that emerged from all European universities combined was the highest in the number of articles (19 articles).

Overall, exploring the impact of innovativeness in AI chatbot usage provides valuable theoretical insights and opens avenues for future research, enhancing our understanding of technology adoption in this rapidly evolving field. This research significantly contributes to the understanding of innovativeness’s impact on behavioral intention and behavior in AI chatbot usage. While prior studies focused mainly on business contexts (BARIŞ, 2020; Heo & Lee, 2018; Selamat & Windasari, 2021), applying these insights to AI chatbots is relatively new, marking this study as a pioneer. Innovativeness as a determinant in this realm is novel for several reasons.

The model also highlights the potential of AICs in language learning, particularly in terms of providing immediate feedback, and fostering a supportive learning environment. The landscape of mobile-application language learning (MALL) has been significantly reshaped in recent years with the incorporation of AICs (Pham et al., 2018). This innovative approach to mobile learning has been positively received by both students and teachers. For example, Chen et al. (2020) highlighted the effectiveness of AICs for Chinese vocabulary learning by comparing chatbot-based tutoring with traditional classroom settings.

2 RQ2: What platforms do the proposed chatbots operate on?

Before you implement your first chatbot, you should make a list of your company’s issues that you want the bot to solve. Organize them by topic and write down everything you’re struggling with. Chatbots also need frequent optimization and maintenance to work properly. Whenever you’re changing anything at your company, you need to reflect that change in your bot’s answers to clients. You should also frequently look through the chats to see what improvements you should implement to your bot.

Chatbots can provide students with immediate feedback, assisting the metacognitive processes of learning (Chang et al., 2022; Cunningham-Nelson et al., 2019; Guo et al., 2022; Okonkwo & Ade-Ibijola, 2021; Wollny et al., 2021). Similar feedback functions are incorporated on a smaller scale into software applications such as Grammarly, Microsoft Word, and Google Docs. Utilizing chatbots, students can make their statements more clear and concise (Cunningham-Nelson et al., 2019) and receive assistance solving difficult problems (Kaur et al., 2021). In one study, students used chatbots to provide continuous feedback on their argumentative essays to assist with writing (Guo et al., 2022). Typically, this feedback is received after peer review or first draft submissions rather than concurrently within the writing process. Students can make revisions and reflect on their learning without the need to interact with their teacher (Cabales, 2019), which can sometimes be difficult in an online learning environment where interactions with teachers are limited (Chang et al., 2022).

Intriguingly, one article was published in Computers in Human Behavior journal. The remaining journal articles were published in several venues such as IEEE Transactions on Affective Computing, Journal of Educational Psychology, International Journal of Human-Computer Studies, ACM Transactions on Interactive Intelligent System. Most of these journals are ranked Q1 or Q2 according to Scimago Journal and Country Rank Footnote 7. After defining the criteria, our search query was performed in the selected databases to begin the inclusion and exclusion process.

Users should stay informed about the latest developments and best practices in AI ethics. They should strive to understand the limitations and capabilities of chatbots and contribute to the responsible and ethical use of AI technologies. Users are responsible for how they use the content generated by chatbots when interacting with it. They should ensure that the information they provide and how they use the model aligns with ethical standards and legal obligations.

Support

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). Firstly, it aims to investigate the current knowledge and opinions of language teacher candidates regarding App-Integrated Chatbots (AICs). Secondly, it seeks to measure their level of satisfaction with four specific AICs after a 1-month intervention. Lastly, it aims to evaluate their perspectives on the potential advantages and drawbacks of AICs in language learning as future educators. In terms of the interaction style, the vast majority of the chatbots used a chatbot-driven style, with about half of the chatbots using a flow-based with a predetermined specific learning path, and 36.11% of the chatbots using an intent-based approach. Only four chatbots (11.11%) used a user-driven style where the user was in control of the conversation.

This helps to decrease the waiting time for your customer support down to a couple of seconds. They perform some rule-based tasks, but they can also detect the context and user intent. They are the best-balanced tool for a business to interact with website visitors.

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. Therefore, future studies should look into educators’ challenges, needs, and competencies and align them in fulfill EC facilitated learning goals. Furthermore, there is much to be explored in understanding the complex dynamics of human–computer interaction in realizing such a goal, especially educational goals that are currently being influenced by the onset of the Covid-19 pandemic. Conversely, future studies should look into different learning outcomes, social media use, personality, age, culture, context, and use behavior to understand the use of chatbots for education. The research significantly enhances theoretical understanding by substantiating the relationships between constructs like knowledge acquisition and application, individual impact, and benefits in the AI chatbot context. Previously, educational and organizational literature recognized the importance of knowledge acquisition and application for performance (Al-Emran et al., 2018; Al-Emran & Teo, 2020; Bhatt, 2001; Grant, 1996; Heisig, 2009).

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. Peer agents can also scaffold an educational conversation with other human peers. Research has demonstrated the promising potential of chatbots in education.

It uses information from trusted sources and offers links to them when users ask questions. YouChat also provides short bits of information and important facts to answer user questions quickly. Claude is a noteworthy chatbot to reference because of its unique characteristics. It offers many of the same features but has chosen to specialize in a few areas where they fall short. It has a big context window for past messages in the conversation and uploaded documents.

Chatbots and Artificial Intelligence in Education

Likewise, ECs can also be used purely for administrative purposes, such as delivering notices, reminders, notifications, and data management support (Chocarro et al., 2021). Moreover, it can be a platform to provide standard information such as rubrics, learning resources, and contents (Cunningham-Nelson et al., 2019). According to Meyer von Wolff et al (2020), chatbots are a suitable instructional tool for higher education and student are acceptive towards its application. Expanding on the necessity for improved customization in AICs, the integration of different features can be proposed to enhance chatbot-human personalization (Belda-Medina et al., 2022). 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.

Participants were third-year-college students enrolled in two subjects on Applied Linguistics taught over the course of 4 months, with two-hour sessions being held twice a week. 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.

A scripted chatbot, also called a rule-based chatbot, can engage in conversations by following a decision tree that has been mapped out by the chatbot designer, and follow an if/then logic. In contrast, NLP chatbots, which use Artificial Intelligence, make sense of what the person writes and respond accordingly (NLP stands for Natural Language Processing). Based on my initial explorations of the current capabilities and limitations of both types of chatbots, I opted for scripted chatbots. Most learning happens in the 99.9% of our lives when we are not in a classroom. 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.

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. Hobert and Meyer von Wolff (2019), Pérez et al. (2020), and Hwang and Chang (2021) examined the evaluation methods used to assess the effectiveness of educational chatbots. The authors identified that several evaluation methods such as surveys, experiments, and evaluation studies measure acceptance, motivation, and usability.

Multilingual support

Table 5 shows the results of the three items included in the DEX dimension. Thanks to these advances, the incorporation of chatbots into language learning applications has been on the rise in recent years (Fryer et al., 2020; Godwin-Jones, 2022; Kohnke, 2023). The wide accessibility of chatbots as virtual language tutors, regardless of temporal and spatial constraints, represents a substantial advantage over human instructors. In terms of the educational role, slightly more than half of the studies used teaching agents, while 13 studies (36.11%) used peer agents. Only two studies presented a teachable agent, and another two studies presented a motivational agent. Teaching agents gave students tutorials or asked them to watch videos with follow-up discussions.

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. Thomas (2020) discussed the benefits of educational chatbots for learners and educators, showing that the chatbots are successful educational tools, and their benefits outweigh the shortcomings and offer a more effective educational experience.

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.

This choice can be explained by the flexibility the web platform offers as it potentially supports multiple devices, including laptops, mobile phones, etc. In general, the followed approach with these chatbots is asking the students questions to teach students certain content. 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).

If you want to see why people switch away from it, reference our ChatGPT alternatives guide, which shares more. Organizations continue to see returns in the business areas in which they are using AI, and

they plan to increase investment in the years ahead. We see a majority of respondents reporting AI-related revenue increases within each business function using AI. And looking ahead, more than two-thirds expect their organizations to increase their AI investment over the next three years. AI high performers are expected to conduct much higher levels of reskilling than other companies are. Respondents at these organizations are over three times more likely than others to say their organizations will reskill more than 30 percent of their workforces over the next three years as a result of AI adoption.

Copilot extends to multiple surfaces and is usable on its own landing page, in Bing search results, and increasingly in other Microsoft products and operating systems. It offers quick actions to modify responses (shorten, sound more professional, etc.). The Gemini Chat GPT update is much faster and provides more complex and reasoned responses. Check out our detailed guide on using Bard (now Gemini) to learn more about it. Chatsonic is the sister product that lets users chat with its AI instead of only using it for writing.

Master Tidio with in-depth guides and uncover real-world success stories in our case studies. Discover the blueprint for exceptional customer experiences and unlock new pathways for business success. Chatbots are exclusively designed to push brand values to an extensive range of prospects.

Education chatbots are interactive artificial intelligence (AI) applications utilized by EdTech companies, universities, schools, and other educational institutions. They serve as virtual assistants, aiding in student instruction, paper assessments, data retrieval for both students and alumni, curriculum updates, and coordinating admission processes. Interestingly, the percentage of students that found “interaction with lecturer” and “online feedback and guidance” for the EC was higher than the control group, and this may be reflected as a tendency to perceive the chatbot as an embodiment of the lecturer.

Literature Review

Last but not least, create a great first impression by greeting your clients with a warm welcome message. So, you’ve seen all the advantages and disadvantages of chatbots in depth. Keep in mind that https://chat.openai.com/ about 74% of clients use multiple channels to start and complete a transaction. So, try to implement your bot into different platforms where your customers can be looking for you and your help.

However, this study contributes more comprehensive evaluation details such as the number of participants, statistical values, findings, etc. 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. 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.

• Similarly, the chatbot in (Schouten et al., 2017) shows various reactionary emotions and motivates students with encouraging phrases such as “you have already achieved a lot today”.
• 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.
• Voice-based chatbots are more accessible to older adults and some special-need people (Brewer et al., 2018).

For example, they can be very good at handling routine queries and qualifying leads. Chatbots must be designed with strict privacy and security controls to safeguard sensitive information. It is a superfast virtual agent that can accurately reply to customer inquiries.

Within this interdisciplinary domain, AI chatbots have emerged as a pivotal application, particularly within educational settings. Six (16.66%) articles presented educational chatbots that exclusively operate on a mobile platform (e.g., phone, tablet). Examples include Rexy (Benedetto & Cremonesi, 2019), which helps students enroll in courses, shows exam results, and gives feedback. Another example is the E-Java Chatbot (Daud et al., 2020), a virtual tutor that teaches the Java programming language.

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 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). The CHISM results, particularly in the Language Experience (LEX) dimension, revealed significant insights about the teacher candidates’ perceptions of the four evaluated chatbots. When examining why none of the AICs achieved moderate satisfaction in the LEX dimension, it is crucial to consider each AIC’s design and target audience limitations, as pointed out in previous research (Gokturk, 2017; Hajizadeh, 2023).

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. 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. This assessment was aligned with the CHISM scale, which was completed in a post-survey. A minimum interaction of three hours per week with each AIC, or 48 h over a month across all AICs, was requested from each participant. The research, conducted over two academic years (2020–2022) with a mixed-methods approach and convenience sampling, initially involved 163 students from the University of X (Spain) and 86 from the University of X (Czech Republic). However, the final participant count was 155 Spanish students and 82 Czech students, as some declined to participate or did not submit the required tasks.

University chatbots took on even greater importance during the height of the COVID-19 pandemic, when reinforcing any kind of connection between students and their campus was a major challenge. At last, we could have missed articles that report an educational chatbot that could not be found in the selected search databases. To deal with this risk, we searched manually to identify significant work beyond the articles we found in the search databases. Nevertheless, the manual search did not result in any articles that are not already found in the searched databases. 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.

Here, we discuss some of the advantages, opportunities, and challenges of chatbots in primary, secondary, and higher education. It should be noted that sometimes chatbots fabricate information, a process called “hallucination,” so, at least for the time being, references and citations should be carefully verified. In an experiment in which the chatbot is asked to design a trendy women’s shoe, it offers several possible alternatives and then, when asked, serially and skillfully refines the design. It was observed that communicating merely was not the main priority anymore as cooperation towards problem-solving is of utmost importance. Example feedback is such as “I learn to push myself more and commit to the project’s success.” Nevertheless, in both groups, all the trends are almost similar.

In the context of using AI chatbots, such as ChatGPT, students who are more innovative (who have higher levels of innovativeness) demonstrated a stronger intention to use and an actual higher usage of the chatbot. This could be interpreted to mean that these students are more inclined to explore and make use of new technologies in their learning processes, which in turn influences their behavior in a positive manner. Furthermore, the positive correlation between innovativeness and behavioral intention may also be linked to the tendency of innovative individuals to perceive less risk in trying out new technologies. This lack of perceived risk, coupled with their natural proclivity towards novelty, may increase their intention to utilize AI chatbots. However, the precise mechanisms through which innovativeness operates in the AI context warrant further study.

Let’s move on to find out what some of the benefits chatbots can bring to your customers. These include answering candidates’ questions and keeping them informed. One of the use cases for this benefit is using a retail chatbot to offer personalized product recommendations and help to place an order. Chatbots can also push your visitor further down the sales funnel and offer assistance with delivery tracking and other support.

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. This study applies an benefits of chatbots in education 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.