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

In general, the studies conducting evaluation studies involved asking participants to take a test after being involved in an activity with the chatbot. The results of the evaluation studies (Table 12) point to various findings such as increased motivation, learning, task completeness, and high subjective satisfaction and engagement. One of them presented in (D’mello & Graesser, 2013) asks the students a question, then waits for the student to write an answer. Then the motivational agent reacts to the answer with varying emotions, including empathy and approval, to motivate students.

• Furthermore, as for constructive feedback, the outcomes for both groups were very similar as the critiques were mainly from the teammates and the instructor, and the ECs were not designed to critique the project task.
• A systematic review follows a rigorous methodology, including predefined search criteria and systematic screening processes, to ensure the inclusion of relevant studies.
• 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.
• To sum up, Table 2 shows some gaps that this study aims at bridging to reflect on educational chatbots in the literature.

The questionnaires used mostly Likert scale closed-ended questions, but a few questionnaires also used open-ended questions. Another interesting study was the one presented in (Law et al., 2020), where the authors explored how fourth and fifth-grade students interacted with a chatbot to teach it about several topics such as science and history. The students appreciated that the robot was attentive, curious, and eager to learn.

Integrating chatbots in education: insights from the Chatbot-Human Interaction Satisfaction Model (CHISM)

We continued our filtering process by reading the candidate publications’ full texts resulting in 74 publications that were used for our review. Compared to 3.619 initial database results, the proportion of relevant publications is therefore about 2.0%. In the case of Google Scholar, the number of results sorted by relevance per query was limited to 300, as this database also delivers many less relevant works. The value was determined by looking at the search results in detail using several queries to exclude as few relevant works as possible. This approach showed promising results and, at the same time, did not burden the literature list with irrelevant items.

All conversations are anonymous so no data is tracked to the user and the database only logs the timestamp of each conversation. With software like DialogFlow, no coding or prior experience is necessary for a basic, text-based build. However, it is recommended that someone with close knowledge of the content have primary editing access to the chatbot.

Enhanced student engagement through chatbot interactions

Additionally, ChatBot excels in lead generation and qualification, proactively engaging customers and integrating with CRMs for a smoother sales process. It helps improve customer experiences by providing personalized interactions and increasing conversion rates. I meticulously ranked and reviewed the 9 best options in 2024, ensuring you make an informed decision for your business. Customer service chatbots are an essential tool for teams to provide top-notch support without breaking the bank. Imagine slashing your customer support expenses by up to 30% – a reality made possible by the latest advancements in chatbot technology. Streamline the sales process by gathering all the essential information before your sales agent jumps into the chat with lead-generation questions.

Subsequently, we delve into the methodology, encompassing aspects such as research questions, the search process, inclusion and exclusion criteria, as well as the data extraction strategy. 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. Furthermore, in regard to problems faced, it was observed that in the EC group, the perception transformed from collaboration issues towards communicative issues, whereas it was the opposite for the CT group.

Automate trivial questions

Chatbots, also known as conversational agents, enable the interaction of humans with computers through natural language, by applying the technology of natural language processing (NLP) (Bradeško & Mladenić, 2012). In fact, the size of the chatbot market worldwide is expected to be 1.23 billion dollars in 2025 (Kaczorowska-Spychalska, benefits of chatbots in education 2019). In the US alone, the chatbot industry was valued at 113 million US dollars and is expected to reach 994.5 million US dollars in 2024 Footnote 1. 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.

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. Peer agents allowed students to ask for help on demand, for instance, by looking terms up, while teachable agents initiated the conversation with a simple topic, then asked the students questions to learn.

AI as a Tool for Learning

While not super-sophisticated, the tool has been well received, at least as a way to get students over that initial hesitance to make some connections. Staff should be able to see AI tools as offering opportunities to shift the focus away from rote or semi-rote tasks to higher-order work and enabling new ways to improve customer service, focus on more strategic efforts, and add value in innovative ways. These advances do not necessarily mean that employees’ jobs are going away but, rather, that AI will enhance how they do their work. Rapid developments in artificial intelligence (AI) represent a significant opportunity to enhance and magnify the effect of Dx projects. AI can act as an accelerant that boosts the value of Dx efforts through its powerful and broad influence on core components of Dx initiatives, ushering in a new chapter of digital transformation on our campuses.

AI and chatbots have a huge potential to transform the way students interact with learning. They promise to forever change the learning landscape by offering highly personalized experiences for students through tailored lessons. With a one-time investment, educators can leverage a self-improving algorithm to design online courses and study resources that go beyond the one-size-fits-all approach, dismantling the age-old education structures. Chatbots will be virtual assistants that offer instant help and answer questions whenever students get stuck understanding a concept.

1 Research questions

Instructors can gather anonymous feedback either on a granular level (eg, regarding a particular class session), or more generally (eg, about the arc of learning over an entire course). More generalized feedback chatbots have the advantage of reuse from session-to-session or year-to-year. Instructors can read through anonymous conversations to get a sense of how the chatbot is being utilized and the nature of inquiries coming into the chatbot.