The impact of educational chatbot on student learning experience Education and Information Technologies

It was targeted to be used as a task-oriented (Yin et al., 2021), content curating, and long-term EC (10 weeks) (Følstad et al., 2019). Students worked in a group of five during the ten weeks, and the ECs’ interactions were diversified to aid teamwork activities used to register group members, information sharing, progress monitoring, and peer-to-peer feedback. According to Garcia Brustenga et al. (2018), EC can be designed without educational intentionality where it is used purely for administrative purposes to guide and support learning. The ECs were also developed based on micro-learning strategies to ensure that the students do not spend long hours with the EC, which may cause cognitive fatigue (Yin et al., 2021). Furthermore, the goal of each EC was to facilitate group work collaboration around a project-based activity where the students are required to design and develop an e-learning tool, write a report, and present their outcomes. Next, based on the new design principles synthesized by the researcher, RiPE was contextualized as described in Table 5.

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.

Companies must consider how these AI-human dynamics could alter consumer behavior, potentially leading to dependency and trust that may undermine genuine human relationships and disrupt human agency. They need to act responsibly about the long-term consequences of customers forming emotional bonds with their AI systems instead of human representatives, as this is a matter of safety that falls under their responsibility and could be likened to manipulation. He expected to find some, since the chatbots are trained on large volumes of data drawn from the internet, reflecting the demographics of our society. Find critical answers and insights from your business data using AI-powered enterprise search technology. Security and data leakage are a risk if sensitive third-party or internal company information is entered into a generative AI chatbot—becoming part of the chatbot’s data model which might be shared with others who ask relevant questions. This could lead to data leakage and violate an organization’s security policies.

The remaining articles (13 articles; 36.11%) present chatbot-driven chatbots that used an intent-based approach. The matching could be done using pattern matching as discussed in (Benotti et al., 2017; Clarizia et al., 2018) or simply by relying on a specific conversational tool such as Dialogflow Footnote 9 as in (Mendez et al., 2020; Lee et al., 2020; Ondáš et al., 2019). In general, the followed approach with these chatbots is asking the students questions to teach students certain content. 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.

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. Participation was voluntary, and students who actively engaged with the chatbots and completed all tasks, Chat GPT including submitting transcripts and multiple-date screenshots, were rewarded with extra credits in their monthly quizzes. This approach ensured higher participation and meaningful interaction with the chatbots, contributing to the study’s insights into the effectiveness of AICs in language education. These real-life examples showcase how chatbots are integrated into education and online schools, offering enhanced learning experiences, administrative support, and improved communication.

Example educational use cases for chatbots

These AI-driven tools create an inclusive studying environment by catering to diverse educational styles and abilities. They offer adaptable content formats, such as audio, visual, and text-based materials, ensuring accessibility for all users, regardless of their needs. Chatbots serve as valuable assistants, optimizing resource allocation in educational institutions.

This kind of availability ensures that learners and educators can access essential information and support whenever they need it, fostering a seamless and uninterrupted learning experience. The primary goal of educational institutions is to provide a high-quality learning experience that equips students with the knowledge and skills they need to succeed. Educational chatbots, designed for education, are a powerful tool to achieve this goal by offering several advantages over traditional teaching methods. Table 7 provides a summary of the primary advantages and drawbacks of each AIC, along with their correlation to the items in the CHISM model, which are indicated in parentheses. 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.

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. Existing literature review studies attempted to summarize current efforts to apply chatbot technology in education. For example, Winkler and Söllner (2018) focused on chatbots used for improving learning outcomes. On the other hand, Cunningham-Nelson et al. (2019) discussed how chatbots could be applied to enhance the student’s learning experience.

Teachers and learners’ views on the use of AICs for language learning

Conversational agents have been developed over the last decade to serve a variety of pedagogical roles, such as tutors, coaches, and learning companions (Haake & Gulz, 2009). Furthermore, conversational agents have been used to meet a variety of educational needs such as question-answering (Feng et al., 2006), tutoring (Heffernan & Croteau, 2004; VanLehn et al., 2007), and language learning (Heffernan & Croteau, 2004; VanLehn et al., 2007). Nonetheless, the existing review studies have not concentrated on the chatbot interaction type and style, the principles used to design the chatbots, and the evidence for using chatbots in an educational setting.

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. The process of organizing your knowledge, teaching it to someone, and responding to that person reinforces your own learning on that topic (Carey, 2015).

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. Studies like those by Chocarro et al. (2023) have delved into students’ enjoyment and engagement with chatbots, highlighting the importance of bot proactiveness and individual user characteristics in shaping students’ satisfaction with chatbots in educational settings. When interacting with students, chatbots have taken various roles such as teaching agents, peer agents, teachable agents, and motivational agents (Chhibber & Law, 2019; Baylor, 2011; Kerry et al., 2008).

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. This cost-effective approach ensures that educational resources are utilized efficiently, ultimately contributing to more accessible and affordable education for all. Education as an industry has always been heavy on the physical presence and proximity of learners and educators. Although a lot of innovative technology advancements were made, the industry wasn’t as quick to adopt until a few years back. Many prestigious institutions like Georgia Tech, Stanford, MIT, and the University of Oxford are actively diving into AI-related projects, not just as topics of research but as initiatives to help make learning more effective and easy.

A not-for-profit organization, IEEE is the world’s largest technical professional organization dedicated to advancing technology for the benefit of humanity.© Copyright 2024 IEEE – All rights reserved. You need to either educational chatbots install a plugin from a marketplace or copy-paste a JavaScript code snippet on your website. If you decide to build a chatbot from scratch, it would take on average 4 to 6 weeks with all the testing and adding new rules.

It’s important to note that some papers raise concerns about excessive reliance on AI-generated information, potentially leading to a negative impact on student’s critical thinking and problem-solving skills (Kasneci et al., 2023). For instance, if students consistently receive solutions or information effortlessly through AI assistance, they might not engage deeply in understanding the topic. With artificial intelligence, the complete process of enrollment and admissions can be smoother and more streamlined. Administrators can take up other complex, time-consuming tasks that need human attention. While many different chatbots and LLMs exist, we choose to highlight four prominent chatbots currently available for free.

Research questions

Finally, the chatbot discussed by (Verleger & Pembridge, 2018) was built upon a Q&A database related to a programming course. Nevertheless, because the tool did not produce answers to some questions, some students decided to abandon it and instead use standard search engines to find answers. 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).

For example, you might prompt the chatbot to create a realistic ethical dilemma that applies to the discipline or to role-play as a patient or client in a relevant scenario. One of the best ways to find a company you can trust is by asking friends for recommendations. The same goes for chatbot providers but instead of asking friends, you can read user reviews. They give you a pretty good understanding of how the company deals with complaints and functionality issues.

One practical approach could be the introduction of specific learning modules on different types of chatbots, such as app-integrated, web-based, and standalone tools, as well as Artificial Intelligence, into the curriculum. Such modules would equip students and future educators with a deeper understanding of these technologies and how they can be utilized in language education. The implications of these findings are significant, as they provide a roadmap for the development of more effective and engaging AICs for language learning in the future. The first question identifies the fields of the proposed educational chatbots, while the second question presents the platforms the chatbots operate on, such as web or phone-based platforms. The third question discusses the roles chatbots play when interacting with students. The fourth question sheds light on the interaction styles used in the chatbots, such as flow-based or AI-powered.

In view of that, it is worth noting that the embodiment of ECs as a learning assistant does create openness in interaction and interpersonal relationships among peers, especially if the task were designed to facilitate these interactions. Modern AI chatbots now use natural language understanding (NLU) to discern the meaning of open-ended user input, overcoming anything from typos to translation issues. Advanced AI tools then map that meaning to the specific “intent” the user wants the chatbot to act upon and use conversational AI to formulate an appropriate response.

• For instance, researchers have enabled speech at conversational speeds for stroke victims using AI systems connected to brain activity recordings.
• 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).
• However, a few participants pointed out that it was sufficient for them to learn with a human partner.

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. While chatbots serve as valuable educational tools, they cannot replace teachers entirely.

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 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). This model was specifically adapted for this study to be implemented with AICs. 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 integration of AI with human cognition and emotion marks the beginning of a new era — one where machines not only enhance certain human abilities but also may alter others. The world is on the verge of a profound transformation, driven by rapid advancements in Artificial Intelligence (AI), with a future where AI will not only excel at decoding language but also emotions. IBM Consulting brings deep industry and functional expertise across HR and technology to co-design a strategy and execution plan with you that works best for your HR activities. The Research Group on Higher Education Learning Practices at Stockholm University engages in theoretical and empirical research on different aspects of higher education.

What are educational chatbots?

According to Adamopoulou and Moussiades (2020), it is impossible to categorize chatbots due to their diversity; nevertheless, specific attributes can be predetermined to guide design and development goals. For example, in this study, the rule-based approach using the if-else technique (Khan et al., 2019) was applied to design the EC. The rule-based chatbot only responds to the rules and keywords programmed (Sandoval, 2018), and therefore designing EC needs anticipation on what the students may inquire about (Chete & Daudu, 2020).

It’s straightforward to use so you can customize your bot to your website’s needs. You can design pre-configured workflows, business FAQs, and other conversation paths quickly with no programming knowledge. This AI chatbots platform comes with NLP (Natural Language Processing), and Machine Learning technologies. Design the conversations however you like, they can be simple, multiple-choice, or based on action buttons. ManyChat is a cloud-based chatbot solution for chat marketing campaigns through social media platforms and text messaging.

In particular, chatbots can efficiently conduct a dialogue, usually replacing other communication tools such as email, phone, or SMS. In banking, their major application is related to quick customer service answering common requests, as well as transactional support. The research also shows that while AI chatbots are being explored across various disciplines, there is no consistent framework for understanding their effects on education. Replication studies are needed to determine how students engage with chatbots and how such interaction may affect their learning. Teachers are skeptical to the value AI chatbots bring to teaching and learning practices.

This suggests that the empirical work does not yet offer insights into the mechanisms of learning that chatbots may facilitate. 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 https://chat.openai.com/ images below you can see two sections of the flowchart of one of my chatbots. In the first one you can see that the chatbot is asking the person how they are feeling, and responding differently according to their answer. 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.

Enhanced student engagement through chatbot interactions

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. 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. 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. Finally, researchers should explore EUD tools that allow non-programmer educators to design and develop educational chatbots to facilitate the development of educational chatbots.

Pedagogical agents, also known as intelligent tutoring systems, are virtual characters that guide users in learning environments (Seel, 2011). 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).

But staffing customer service departments to meet unpredictable demand, day or night, is a costly and difficult endeavor. The time it takes to build an AI chatbot can vary based the technology stack and development tools being used, the complexity of the chatbot, the desired features, data availability—and whether it needs to be integrated with other systems, databases or platforms. With a user-friendly, no-code/low-code platform AI chatbots can be built even faster. The earliest chatbots were essentially interactive FAQ programs, which relied on a limited set of common questions with pre-written answers.

Moving on, we present a comprehensive analysis of the results in the subsequent section. Finally, we conclude by addressing the limitations encountered during the study and offering insights into potential future research directions. Firstly, Kearney et al. (2009) explained that in homogenous teams (as investigated in this study), the need for cognition might have a limited amount of influence as both groups are required to be innovative simultaneously in providing project solutions. Lapina (2020) added that problem-based learning and solving complex problems could improve the need for cognition. Hence, when both classes had the same team-based project task, the homogenous nature of the sampling may have attributed to the similarities in the outcome that overshadowed the effect of the ECs.

Such a contribution also offers networking opportunities and support for current students. Additionally, this will positively impact the brand image, attracting potential applicants and stakeholders. Through AI and ML capabilities, bots help to access relevant materials and submit tasks. Implementing innovative technologies, establishments will ensure continuous learning beyond the classroom.

This combination enables AI systems to exhibit behavioral synchrony and predict human behavior with high accuracy. Improve customer engagement and brand loyalty

Before the advent of chatbots, any customer questions, concerns or complaints—big or small—required a human response. Naturally, timely or even urgent customer issues sometimes arise off-hours, over the weekend or during a holiday.

Also, AI chatbots contribute to skills development by suggesting syntactic and grammatical corrections to enhance writing skills, providing problem-solving guidance, and facilitating group discussions and debates with real-time feedback. Overall, students appreciate the capabilities of AI chatbots and find them helpful for their studies and skill development, recognizing that they complement human intelligence rather than replace it. From the viewpoint of educators, integrating AI chatbots in education brings significant advantages. AI chatbots provide time-saving assistance by handling routine administrative tasks such as scheduling, grading, and providing information to students, allowing educators to focus more on instructional planning and student engagement.

They ensure a more interactive and effective student learning method and alleviate teachers’ workload. From homework assistance and personalized tutoring to administrative tasks and language learning, chatbots can potentially revolutionize the educational landscape. 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. This allows the teacher to tweak the chatbot’s design to improve the experience. Equally if not more importantly, it can reveal gaps in knowledge or flawed assumptions the learners hold, which can inform the design of new learning experiences — chatbot-mediated or not. Only four studies (Hwang & Chang, 2021; Wollny et al., 2021; Smutny & Schreiberova, 2020; Winkler & Söllner, 2018) examined the field of application.

Considering this, the University of Murcia in Spain used an AI chat assistant that successfully addressed more than 38,708 inquiries with an accuracy rate of 91%. By transforming lectures into conversational messages, such tools enhance engagement. This method encourages students to ask questions and actively participate in processes comfortably. As a result, it significantly increases concentration level and comprehensive understanding.

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 language proficiency of the students aligned with the upper intermediate (B2) and advanced (C1) levels as defined by the Common European Framework of Reference for Languages (CEFR), while some participants were at the native speaker (C2) level. In our study, the primary focus was on evaluating language teacher candidates’ perceptions of AICs in language learning, rather than assessing language learning outcomes. Considering that the majority of participants possessed an upper intermediate (B2-C1) or advanced (C2) proficiency level, the distinction between native and non-native speakers was not deemed a crucial factor for this research. Subsequently, a statistical analysis was conducted to evaluate the impact of language nativeness (Spanish and Czech versus non-Spanish and non-Czech speakers), revealing no significant differences in the study’s outcomes.

Chatbots deployed through MIM applications are simplistic bots known as messenger bots (Schmulian & Coetzee, 2019). These platforms, such as Facebook, WhatsApp, and Telegram, have largely introduced chatbots to facilitate automatic around-the-clock interaction and communication, primarily focusing on the service industries. Even though MIM applications were not intended for pedagogical use, but due to affordance and their undemanding role in facilitating communication, they have established themselves as a learning platform (Kumar et al., 2020; Pereira et al., 2019). Accordingly, chatbots popularized by social media and MIM applications have been widely accepted (Rahman et al., 2018; Smutny & Schreiberova, 2020) and referred to as mobile-based chatbots.

You can foun additiona information about ai customer service and artificial intelligence and NLP. 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. The implications of the research findings for policymakers and researchers are extensive, shaping the future integration of chatbots in education.

The fifth question addresses the principles used to design the proposed chatbots. The sixth question focuses on the evaluation methods used to prove the effectiveness of the proposed chatbots. Finally, the seventh question discusses the challenges and limitations of the works behind the proposed chatbots and potential solutions to such challenges.