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.

This research-to-practice full paper proposes an automated solution for forming groups of students in computing higher education with the Problem-Based Learning (PBL) approach. PBL is a pedagogical model that promotes the development of professional skills (knowledge, skills, and attitudes) for problem-solving through collaborative learning. In this context, effective team building is essential to the success of PBL, as it can enhance student learning and development. The formation of teams using the PBL approach involves effort and observation of several aspects, which makes manual grouping inefficient in formatting balanced groups. In addition, both the instructor and the student can participate in the grouping decision process, considering the constraints defined by the instructor and the student's satisfaction with the recommendations made. In this sense, when using an automated approach that considers these criteria and possibilities for forming groups, it is possible to streamline the process of structuring teams and mitigate the formation of groups with low potential for success. This study proposes an automated solution for forming groups of students using the DSR (Design Science Research) method applied in evolutionary cycles. It considers the following research question: RQ) How can balanced groups in computing higher education with PBL be formed automatically, considering predefined criteria, attributes, and affinities between their members? This solution considers multiple attributes of the individuals involved and the organizer's criteria for dividing groups, using a semantic structure for group formation. The solution also allows students to negotiate based on affinity with peers and satisfaction with the group. System prototypes were created and evaluated throughout the DSR cycles to evaluate the proposal, demonstrating compliance with the defined restrictions and indication of balanced teams. As primary limitations, we point out challenges in student data collection that hinder group formation. The DSR method iteratively improved solutions, but more application cycles will be necessary to ensure the solution's robustness. In future work, further analysis should link team performance to group formation, focusing on criteria for balanced groups in PBL courses.

This research full paper proposes an intelligent tutoring system (ITS) to support the study of software project management, considering the students' learning styles. The learning process is a complex and multifaceted phenomenon that varies significantly from one person to another. Everyone absorbs knowledge differently because each has learning characteristics that directly impact the learning process. Ignoring these differences can result in inefficient and demotivating teaching. Therefore, teachers must invest time in understanding each student's learning style, strengths, and weaknesses, which can be overwhelming, especially in classrooms with a high student-teacher ratio. In this scenario, AI-based technology can be a strong ally. For example, chatbot technology offers an opportunity to adapt to these individual differences and to enable a more engaging and friendly approach for students, contributing to an active and interactive learning environment when exercising the role of a study companion and, why not, a bit of study guide. In this context, this paper proposes developing and applying a chatbot-based ITS called Juh to identify each student's learning style and, based on this understanding, present study activities appropriate to their learning style. The ITS uses meaningful learning theory to personalize the learning journey, detecting five profiles: active, constructive, intentional, authentic, and cooperative. This study chose project management (PM) as its knowledge area, particularly project management methodologies, aiming to evaluate the benefits of this research proposal. In this context, this research proposes to answer the following research question: RQ) How can the study of project management be supported actively and interactively, considering the students' learning styles? As its main contribution, the solution aims to offer support in the introduction to the study of project management and improve the learning experience according to the individual characteristics of each student, maximizing the assimilation of knowledge. Therefore, the difference lies in exploring how and what to learn. The solution was developed using the Design Science Research method and the low code Blip platform. After prototyping and evaluating the ITS with PM and education experts, promising points about the approach were observed, with users highlighting improvements in productivity and ease of use of the tutoring system.

This Research Full Paper reports an experience related to the Training Program 4.0 to encourage technological innovation focused on developing professional skills, especially soft skills, in undergraduate and graduate students. As the primary motivation for this program, the constant technological evolution and consequent demand for continuing education of information technology professionals stand out. Also, considering the complexity of problems solved with technology and an increasingly demanding market, computing education has been transforming, focusing on technical skills, such as cloud technologies and AI, and non-technical skills, such as self-initiative, communication, and collaboration. In this context, continuing education initiatives through teaching and learning approaches centered on practice are beneficial for forming professional skills. This study describes an experience of one of the units of the Training Program 4.0 focused on innovation for the automotive sector, based on the report and discussion of three projects. As a main contribution, the report proposes a PBL process for innovation projects, transforming it into a PBL training that can be planned, executed, monitored, and continuously improved. Using an appropriate assessment model makes it possible to manage soft skills throughout the projects, carrying out necessary interventions. To present evidence of the use of the proposed PBL Process, the results of the projects are discussed, showing how Soft skills can be managed in the context of innovation projects or other educational contexts, such as courses or disciplines.

This Research Full Paper presents a proposal founded on the Constructive Alignment Theory and supported by Artificial Intelligence (AI) technologies, aiming to monitor professional competencies in Computing Higher Education (CHE) based on Problem- Based Learning (PBL). Within CHE, there is a growing movement to change an educational paradigm that goes beyond knowledge-based education. Therefore, active learning methodologies such as the PBL, have become increasingly popular to develop student’s technical knowledge, skills, and attitudes, translating in professional competencies. In this context, this research advocates the Constructive Alignment Theory by Biggs to monitor professional competencies in PBL experiences. Biggs suggests the alignment between the learning results from the student’s perspective and the objectives defined by the teacher in the course planning. This follow-up can be done in various ways and include many data sources like the usual student feedback questionnaires. However, it requires a lot of effort from the teacher/pedagogical team and involves difficulties related to effort, workload, and time spent to make improvements. SO, how to monitor students' professional competencies in an automated way, having as references the Constructive Alignment Theory and the course planning? For this, we propose an AI- based tool for processing student feedback, called SkillSight, helping teachers monitor competencies considering the learning outcomes. From Design Science Research cycles, the first evaluation results showed a good acceptance of this tool and suggestions for improvements, especially at the results visualization.