:async: Which is More effective in Guiding Students' Online Learning Journey, Lecturers or AI-aided Chatbot?

Authors: Yu Cheng Teng, Yung-Hsiang Hu
Institution: National Yunlin University of Science and Technology
Country: Taiwan

Topic: Applications of Open Education Practices/Open Pedagogy/Open Education Research
Sector: Higher Education
UNESCO Area of Focus: Building capacity
Session Format: Presentation


Nowadays, artificial intelligence (AI) could serve as a leading role in tutoring students in the growing trend of learning analytics, providing proactive recommendations for students. In open education, lecturers could not provide instant recommendations for every student which made precision education far less achievable. Recent research has shown that AI tutors are beneficial to individual learning outcomes. The study aims to discuss whether different presentation of learning recommendations would affect learning outcomes. To examine this, we firstly employed three courses in MOOCs (1,278 students) to construct a prediction model for students' learning behaviours during 2017-18 academic years; secondly, we designed two types of recommendation counselling through Chatbot, an AI-aided mechanism where students can receive natural-language representation and logbook guidance. In this study, lecturer’s phone-care were compared with AI-aided students and as a result 129 students from one online course were sampled in 2019-20. A simple random sampling was conducted to divide three groups (43 participants used natural-language representation; 44 participants used logbook guidance; 42 percipients physically received phone calls from their lecturer). This practice has shown that there is no statistical significance in these three groups. The result may provide an alternative teaching modality through technology for lecturers to meet students’ learning unattended support in the open education. In addition, this practice presents the advantage of using Chatbot since it enriches students’ leaning experiences and online teaching efficacy. It underlines the potential values as it is cost-saving and time-saving for educational administrators.


AI, MOOCs, learning analytics, precision education, teaching efficacy

Good morning, ladies and gentlemen. We are from Teaching Excellence Center at National Yunlin University of Science and Technology. Dr. Hu and I look forward to sharing our ideas with all attendees.

Please check out our materials

Thank you Yu Cheng Teng and Yung-Hsiang Hu for your presentation. I am also strongly in favour of using AI for monitoring and guiding the students’ learning experience. My Anytime presentation MOOCs and personalized learning experience is along the same tines. Please take a look at my presentation and leave your comments.

thank you @YunTech for sharing your work with the community. This is definetely the way MOOCs are going and a is ground that needs much further exploring.

Lecturers can not provide instant tutoring services for every student in open education.

Giving online or distance students some predefined guidance is always challenging but rewarding when done with empathy and human touch. Adding AI to this process can make guidance more personalized or contextualized to the reaity of each learner.

I like the conclusion:

The application of ITR in the educational domain is not to replace human teachers but to help students and teachers.

It won’t replace them but do you think teachers’ role will change by integrating learning analytics?

Hello, Mario. As far as I can tell, teachers’ role will be changed from practitioners to mentors, with the latter emphasizing students’ blind spots and long-term career plans. This is possible for open education courses.

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Thank you very much, Subha. We will definitely do and leave some messages.

Yu Cheng