In recent years, rapid advancements in computer science, including increased capabilities of machine learning models like Large Language Models (LLMs) and the accessibility of large datasets, have facilitated the widespread adoption of AI technology, such as ChatGPT, underscoring the need to design and evaluate these technologies with ethical considerations for their impact on students and teachers. Specifically, the rise of Automated Essay Scoring (AES) platforms have made it possible to provide real-time feedback and grades for student essays. Despite the increasing development and use of AES platforms, limited research has specifically focused on AI explainability and algorithm transparency and their influence on the usability of these platforms. To address this gap, we conducted a qualitative study on an AI-based essay writing and grading platform, with a primary focus to explore the experiences of students and graders. The study aimed to explore the usability aspects related to explainability and transparency and their implications for computer science education. Participants took part in surveys, semi-structured interviews, and a focus group. The findings reveal important considerations for evaluating AES systems, including the clarity of feedback and explanations, impact and actionability of feedback and explanations, user understanding of the system, trust in AI, major issues and user concerns, system strengths, user interface, and areas of improvement. These proposed key considerations can help guide the development of effective essay feedback and grading tools that prioritize explainability and transparency to improve usability in computer science education. / Master of Science / In recent years, rapid advancements in computer science have facilitated the widespread adoption of AI technology across various educational applications, highlighting the need to design and evaluate these technologies with ethical considerations for their impact on students and teachers. Nowadays, there are Automated Essay Scoring (AES) platforms that can instantly provide feedback and grades for student essays. AES platforms are computer programs that use artificial intelligence to automatically assess and score essays written by students. However, not much research has looked into how these platforms work and how understandable they are for users. Specifically, AI explainability refers to the ability of AES platforms to provide clear and coherent explanations of how they arrive at their assessments. Algorithm transparency, on the other hand, refers to the degree to which the inner workings of these AI algorithms are open and understandable to users. To fill this gap, we conducted a qualitative study on an AI-based essay writing and grading platform, aiming to understand the experiences of students and graders. We wanted to explore how clear and transparent the platform's feedback and explanations were. Participants shared their thoughts through surveys, interviews, and a focus group. The study uncovered important factors to consider when evaluating AES systems. These factors include the clarity of the feedback and explanations provided by the platform, the impact and actionality of the feedback, how well users understand the system, their level of trust in AI, the main issues and concerns they have, the strengths of the system, the user interface's effectiveness, and areas that need improvement. By considering these findings, developers can create better essay feedback and grading tools that are easier to understand and use.

<p dir="ltr">In the dynamic realm of Artificial Intelligence (AI), this study explores the multifaceted landscape of Trustworthy AI with a dedicated focus on achieving both explainability and acceptance. The research addresses the evolving dynamics of AI, emphasizing the essential role of human involvement in shaping its trajectory.</p><p dir="ltr">A primary contribution of this work is the introduction of a novel "Trustworthy Explainability Acceptance Metric", tailored for the evaluation of AI-based systems by field experts. Grounded in a versatile distance acceptance approach, this metric provides a reliable measure of acceptance value. Practical applications of this metric are illustrated, particularly in a critical domain like medical diagnostics. Another significant contribution is the proposal of a trust-based security framework for 5G social networks. This framework enhances security and reliability by incorporating community insights and leveraging trust mechanisms, presenting a valuable advancement in social network security.</p><p dir="ltr">The study also introduces an artificial conscience-control module model, innovating with the concept of "Artificial Feeling." This model is designed to enhance AI system adaptability based on user preferences, ensuring controllability, safety, reliability, and trustworthiness in AI decision-making. This innovation contributes to fostering increased societal acceptance of AI technologies. Additionally, the research conducts a comprehensive survey of foundational requirements for establishing trustworthiness in AI. Emphasizing fairness, accountability, privacy, acceptance, and verification/validation, this survey lays the groundwork for understanding and addressing ethical considerations in AI applications. The study concludes with exploring quantum alternatives, offering fresh perspectives on algorithmic approaches in trustworthy AI systems. This exploration broadens the horizons of AI research, pushing the boundaries of traditional algorithms.</p><p dir="ltr">In summary, this work significantly contributes to the discourse on Trustworthy AI, ensuring both explainability and acceptance in the intricate interplay between humans and AI systems. Through its diverse contributions, the research offers valuable insights and practical frameworks for the responsible and ethical deployment of AI in various applications.</p>