AI in Education: The Benefits and Limitations of Intelligent Tutoring Systems

Artificial Intelligence (AI) has become increasingly popular in the AI in education sector, and one of its most exciting applications is in the form of Intelligent Tutoring Systems (ITS). These are computer-based systems designed to provide personalized feedback and guidance to learners, based on their individual needs and performance.

While ITS has the potential to revolutionize the way we learn, there are also some limitations to be aware of. In this blog, we will explore the benefits and limitations of ITS in AI in education.

AI in Education Benefits of Intelligent Tutoring Systems in Education

1. Personalized Learning: One of the main benefits of ITS is that it provides personalized learning experiences for each student. The system can adapt to the individual student’s learning style, preferences, and pace, ensuring that they are challenged but not overwhelmed.
2. Immediate Feedback: ITS provides immediate feedback to students, allowing them to identify their mistakes and learn from them quickly. This real-time feedback is not possible with traditional teaching methods.
3. Cost-Effective: ITS can be more cost-effective than hiring additional teachers or tutors. Once the system is developed, it can be used to teach a large number of students at the same time.
4. Consistent Quality: Unlike human teachers, ITS provides consistent quality of instruction. It ensures that all students receive the same level of instruction and feedback, regardless of their location or socioeconomic status.
5. Increased Engagement: ITS can make learning more engaging and interactive. The use of multimedia and interactive elements can keep students interested and motivated, making learning a more enjoyable experience.

AI in Education Limitations of Intelligent Tutoring Systems in Education

Research on intelligent tutoring systems (ITS) has two aims: to provide sophisticated instructional advice on a one-on-one basis that is better than that achieved with conventional computer-aided instruction and is comparable to that of a good human tutor, and to develop and test models about the cognitive processes involved in instruction. The ‘intelligence’ of ITS comes from the application of artificial intelligence techniques which are used in four interacting components: The knowledge base contains the domain knowledge, the student model represents the student’s current knowledge state, the pedagogical module contains suitable instructional measures which are contingent on the content of the student model, and the user interface enables an effective dialog between ITS and student. Usually, the knowledge base is the central part of the instructional process but there is a diversity of approaches that also emphasize the other components. Although research on ITS has produced many interesting theoretical insights, there are relatively few ITS that are used and there are very few that are regularly used in schools. This unsatisfactory state of affairs may be due to researchers’ diversity of interests, missing evaluation studies that show the superiority of ITS, and theoretical problems with the student model. Current, more practically inclined, approaches de-emphasize the reliance on the problematic student model and put more effort into the construction of theory-based user interfaces.

1. Lack of Human Interaction: ITS lacks the human touch, which can be important in developing a student’s emotional intelligence and social skills. The system is unable to provide emotional support or develop personal relationships with students.
2. Limited Scope: ITS can only teach what it has been programmed to teach. It cannot think creatively or improvise, which is something that human teachers are capable of doing.
3. Technical Issues: Technical issues with ITS can disrupt the learning process. If the system is not functioning properly or if there is a connectivity issue, students may not be able to access the material.
4. Initial Development Costs: Developing ITS can be expensive, which may be a challenge for smaller institutions or those with limited budgets.
5. Lack of Flexibility: ITS may not be able to accommodate individual needs or special requests. Students who need specific accommodations may not be able to receive them through an ITS.

Conclusion

Intelligent Tutoring Systems have the potential to revolutionize the way we learn by providing personalized learning experiences, immediate feedback, and consistent quality of instruction. However, there are also some limitations to be aware of, including the lack of human interaction, limited scope, technical issues, initial development costs, and lack of flexibility. It is important to carefully consider these factors when deciding whether to implement ITS in an AI in the educational setting. Ultimately, the decision should be based on the needs and goals of the institution and its students.