Examples of AI use in Open and Distance Education



Karal et al. (2019) assessed an artificial intelligence-based distance education system called ARTIMAT, which was designed to develop mathematical problem-solving skills, in terms of the conceptual competence, the ease of use and students’ contribution to the problem-solving process (Karal et al., 2019). The ARTIMAT was experienced by 59 students in 10th grade in an Anatolian High School in Trabzon. In the following section (3. Advantages and Disadvantages), more features of the AI system will be discussed. However, to explain briefly, the AI system was found to be very effective and satisfying according to the student interviews.



There are also some contexts where AI is used especially for students with special needs (e.g., dyslexia). As Drigas & Dourou (2013) argue, children with dyslexia have special learning skills, and thus most of the time only specialized institutions know better and are able to support their reading difficulties. Then, a software called the AGENT-DYSL was developed by researchers. According to Drigas & Dourou (2013),

the main features of the AI system are:

  • propose a reading system for dyslexic children with personalized attention, through presentation of customized reading materials
  • integrates into school environment
  • takes into account the context of learning

Also, the unique features of the AI software program are:

  • personalized assistance
  • combines speech recognition
  • state recognition via image
  • profiles error via adaptive and personalized support

Thus, it appears that AGENT-DYSL is able to support such particular students in many aspects. We believe this can be applied to more broad contexts where not only students with special needs but also any students are learning. For example, in Japanese public schools, there are usually some students with special needs in classes. Moreover, there are also some students who do not need specific support, but still face some difficulty in learning. Therefore, integration of such AI systems into the school environment would provide students with personalized assistance with taking into account the context of their school environments.

Repeated Reading Adaptive Fluency Tutor (R2 aft)


Similar to the previous AI system, there seems to be a lot of AI systems which particularly focus on reading assistance. The R2 aft tutor (Repeated Reading Adaptive Fluency Tutor) was developed to improve reading fluency among students with dyslexia. Since this AI system is still in the process of evaluation, it is not very used worldwide yet. Therefore, not much information was provided on the Internet. However, according to Drigas & Dourou (2013), an important part of the R2 aft tutor is that it generates a text to be read through a story assembly engine called TASA (Text And Story Assembler).

Spatial Math Tutor


There is also a cognitive tool for better performance on mathematical tasks. The AI system is called Spatial Math Tutor which was developed, tested, and incorporated into an online tutoring environment (Drigas & Dourou, 2013). Through graphical representation and the manipulation of CG objects, the AI system is considered a beneficial tool for learners taking into account all the assistive 3D graphic technology and interaction tutoring (Drigas & Dourou, 2013).

Chatbot for peer-assessment


A chatbot , as an Artificial Intelligence technology, is known as a conversational agent, which refers to a computer program engaging in conversation or simulating informal chat communication between a human and a computer program in natural language (Chak & LugChatter, 2015). And as it was mentioned by Liu et al., (2019), “In the field of education, the role of chatbots has been highlighted in the context of e-learning and has received considerable attention.” In Pereira et al., (2019) ’s research, mobile based chatbots were used to record the voices of the MOOC students, so they can do the peer- assessment with more motivation and participation. According to the research of Pereira et al., (2019), we can imagine that since today’s students tend to rely on their personal device like mobile phone, or social media application, scholars too are beginning to insert AI to Open and Distance Education through a mobile phone assisted learning system. Thus, when talking about AI applications, we should not only think about a computer, but devices like mobile phones should also be considered.

Using AI tutoring agent to teach


In Goel & Joyner (2017) ’s study, they set a foundation online AI course for an online program of an institution to solve the problem of the rapidly growing need for AI courses. And the courses are delivered by the MOOC provider Udacity. In the research, AI was used in two ways: One is intelligent tutoring of AI concept; and another is Authentic engagement in AI research. For the former, exercise were set in the video lesson, “nanotutors” are set to support the exercises. As Goel & Joyner (2017) mentioned, the role of the “nanotutors” is to “ guide students’ understanding of one narrowly defined skill such as completing a semantic network for a particular problem or simulating an agent’s planning in the blocks world”. According to the students satisfaction survey, they found that most of the students agree about the function of the “ nanotutors ” in helping them to understand the material. As for the latter, the AI course can allow students to re-create the AI agents as an authentic engagement. And it helped the students to know the dynamic and emerging theories of AI. Although, this study seems to be a specific one, since it use AI to teach AI. However, we can also gain some enlightenment from the practice that AI may us to teaching itself. Moreover, what we found interesting in this paper is that, it mentioned that the video lesson itself may not be interactive as a general course in a school situation, the discussion part like a forum may play an important role on that part. Therefore, inserting AI to facilitate interaction seems to be an interesting topic in the future study.

Application of AI in distance education in Indonesia


According to Putra & Triastuti (2019), when assessing whether a country has a more positive image on integration of AI into distance educational contexts, “readiness” will be a useful criteria. Specifically, they argue that readiness for the application of AI in distance education must consider the specific influence on each situation, institution or learning program (Putra & Triastuti, 2019). They strongly argue that although various factors have an influence on implementation and effectiveness of AI, “readiness” will be a critical success factor. From this perspective, Putra & Triastuti (2019) analyzed the implementation of AI in distance education in Indonesia. Then, they state that the following points are some issues Indonesia has to deal with at this time.

  • Indonesia needs technical training for teachers.
  • Indonesian teachers need to understand their role as facilitators, collaborators, mentors, trainers and study partners for students in the e-learning process.
  • Indonesian government needs to improve facilities and infrastructure that support distance education in order to facilitate the needs of the latest AI technology, such as:
    • Reach of electricity to the region
    • Fast internet connection
    • Computers with the latest systems