Critical Factors for a Reliable AI in Tutoring Systems on Accuracy, Effectiveness, and Responsibility

In recent years, there has been a surge in the development and use of artificial intelligence (AI) systems in various fields, including education. One such application is the AI-based tutoring system, which can provide personalized learning experiences to students. These systems leverage advanced algorithms to analyze student performance, identify knowledge gaps, and deliver targeted feedback and guidance. One of the significant challenges educators and researchers face in the context of AI-based tutoring systems is the lack of reliability in the systems. The accuracy, effectiveness, and responsibility of AI systems are critical factors that determine their reliability. Accuracy challenges for AI algorithms in tutoring systems include accurately modeling individual learner profiles, providing tailored content that aligns with each student's pace and understanding, addressing diverse learning strategies, and ensuring the feedback is specific and actionable. Overcoming data sparsity and ensuring algorithmic transparency and fairness are also significant hurdles. Challenges in ensuring effectiveness include developing algorithms that accurately adapt to individual learning needs, processing natural language effectively, maintaining engagement, and providing contextually relevant feedback. Responsibility challenges include ensuring data privacy and security, preventing algorithm biases affecting learning outcomes, and maintaining ethical standards in AI interactions. Balancing automation with human oversight to support diverse learning needs without compromising educational integrity is also crucial. Considering these challenges, this study discusses the critical factors for reliable AI in tutoring systems from perspectives of accuracy, effectiveness, and responsibility. The research has a descriptive character and qualitative analysis, applying the systematic literature review (SLR) method. The analysis of 43 studies in the last five years made it possible to find some interesting results. In summary, the accuracy of AI in Tutoring Systems is impacted by data quality and preprocessing, choice of appropriate metrics, advanced learning techniques, dataset diversity, and correct selection of hyperparameters. As for the factors that influence the effectiveness of these systems, the personalization of learning and the ability of the systems to adapt to individual needs through personalized pedagogical interventions stand out, using techniques such as recurrent neural networks to predict the quality of interactions. However, challenges related to understanding learning emotions reinforce the complexity of building effective models based on emotion induction. Concerning the responsible use of AI in tutoring systems, it is crucial to consider the privacy and security of student data, adopt collaborative human-machine approaches, and align the use of AI with institutional governance, promoting a safe and ethical learning environment. As a main contribution, we highlight an enlightening discussion of the critical factors for a reliable AI in the context of tutoring systems, identifying quality studies on the subject to support researchers.