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

Such a strategy was used to ensure that the instructor could guide the students the next day if there were any issues. The teaching and learning in both classes are identical, wherein the students are required to design and develop a multimedia-based instructional tool that is deemed their course project. Students independently choose their group mates and work as a group to fulfill their project tasks. Moreover, both classes were also managed through the institution’s learning management system to distribute notes, attendance, and submission of assignments.

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. 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). 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.

Improved feedback collection using chatbot-driven surveys

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. Oftentimes reflections that students share with the bot are shared with the class without identifiable information, as a starting point for social learning. Although chatbots can provide information, they should not act as a substitute for, instead of spurring the development of students’ critical thinking and analytical skills.

After defining the criteria, our search query was performed in the selected databases to begin the inclusion and exclusion process. The metadata of the studies containing; title, abstract, type of article (conference, journal, short paper), language, and keywords were extracted in a file format (e.g., bib file format). Subsequently, it was imported into the Rayyan tool Footnote 6, which allowed for reviewing, including, excluding, and filtering the articles collaboratively by the authors. 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.

Enhanced student engagement through chatbot interactions

Regarding gender, 81% of the participants were females, while 19% were male students. 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.

Likewise, it was deemed necessary due to the nature of the project, which involves design. Lastly, teamwork perception was defined as students’ perception of how well they performed as a team to achieve their learning goals. According to Hadjielias et al. (2021), the cognitive state of teams involved in digital innovations is usually affected by the task involved within the innovation stages.

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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 benefits of chatbots in education (11.11%) used a user-driven style where the user was in control of the conversation. A user-driven interaction was mainly utilized for chatbots teaching a foreign language. Five articles (13.88%) presented desktop-based chatbots, which were utilized for various purposes.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors. The authors would like to express their gratitude to all the college students from both institutions for their invaluable participation in this project. 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. Users should be cautious about the information generated by chatbots and not rely solely on them as sources of information. They should critically evaluate and fact-check the responses to prevent the spread of misinformation or disinformation.

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. Subsequently, the assessment of specific topics is presented where the user is expected to fill out values, and the chatbot responds with feedback.

However, chatbots can take conversations from start to finish, meaning you don’t necessarily need more head count. A chatbot can give your customers the answers they need and only transfer the chatbot conversation to a human if the customer’s questions go beyond the typical scope. We’ve been using machine learning to build a web-based tool that helps first-year students connect with other first-year students who have similar interests. The tool leverages various pieces of information that we’ve learned about incoming students to suggest possible students with whom they may want to connect.

Examples of these are chatbots simulating a virtual pen pal abroad (Na-Young, 2019). Conversations with this kind of chatbot aim to motivate the students to look up vocabulary, check their grammar, and gain confidence in the foreign language. In terms of the educational role, slightly more than half of the studies used teaching agents, while 13 studies (36.11%) used peer agents.

AI customer support and service chatbots leverage natural language processing and machine learning to provide intelligent responses, making them perfect for answering FAQs, collecting customer data for lead generation, and offering real-time updates. Simply put, customer service chatbots are AI-powered computer programs designed to chat with users, mimicking human conversation. These same customer service chatbots understand and respond to queries, handle routine tasks, and perform specific actions like booking appointments or processing orders.

Chatbots and Artificial Intelligence in Education

Further, none of the articles discussed or assessed a distinct personality of the chatbots though research shows that chatbot personality affects users’ subjective satisfaction. 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. Other chatbots used experiential learning (13.88%), social dialog (11.11%), collaborative learning (11.11%), affective learning (5.55%), learning by teaching (5.55%), and scaffolding (2.77%). Another example is the study presented in (Ondáš et al., 2019), where the authors evaluated various aspects of a chatbot used in the education process, including helpfulness, whether users wanted more features in the chatbot, and subjective satisfaction. The students found the tool helpful and efficient, albeit they wanted more features such as more information about courses and departments. In comparison, 88% of the students in (Daud et al., 2020) found the tool highly useful.

Among them, ChatGPT and Google Bard are among the most profound AI-powered chatbots. 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. They possess the ability to generate text, create diverse creative content, and provide informative answers to questions, although their accuracy may not always be perfect. The key difference is that Google Bard is trained on a dataset that includes text from the internet, while ChatGPT is trained on a dataset that includes text from books and articles.

The Chatbot-Human Interaction Satisfaction Model (CHISM) is a tool previously designed and used to measure participants’ satisfaction with intelligent conversational agents in language learning (Belda-Medina et al., 2022). The pre-post surveys were completed in the classroom in an electronic format during class time to ensure a focused environment for the participants. Quantitative data obtained were analysed using the IBM® SPSS® Statistics software 27. The main objective was to determine the average responses by calculating the means, evaluate the variability in the data by measuring the standard deviation, and assess the distribution’s flatness through kurtosis. The landscape of mobile-application language learning (MALL) has been significantly reshaped in recent years with the incorporation of AICs (Pham et al., 2018).

• Let’s explore the top 9 chatbots leading the charge in revolutionizing customer interactions.
• For example, Winkler and Söllner (2018) focused on chatbots used for improving learning outcomes.
• These new tools represent Dx 2.0 examples that will transform the experiences of our students and the information and insights to which they have access.
• This platform delivers fast, accurate responses by analyzing your website content, ensuring human-like interactions tailored to your business needs.
• The bots can then process this information on the instructor’s request to generate student-specific scorecards and provide learning gap insights.

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). An example of this is the chatbot in (Sandoval, 2018) that answers general questions about a course, such as an exam date or office hours. Assignment of coded research topics identified in related literature reviews to research categories.

Digital Transformation 2.0 builds on the basic tenants of the current Dx model and leverages the rapidly developing AI advances to accelerate transformations and magnify their impact. Maximizing these developments requires a renewed focus on technology, culture, and workforce shifts. Using AI, Dx 2.0 efforts can create positive transformations for our campuses and throughout higher education. At Ithaca College, we have been exploring how these new AI tools can provide insights and services in ways that were not available to us before. We hope these tips and suggestions will help you figure out how GenAI tools might affect your English teaching. Remember that it’s important for all of us to be aware of GenAI tools, how they work, and have the chance to explore and learn about them.