Rademics Research Institute

Abstract

The integration of AI in education has revolutionized student assessment and feedback systems, enabling personalized learning pathways tailored to individual needs. The opacity of AI-driven feedback mechanisms presents significant challenges in transparency, trust, and pedagogical alignment. XAI has emerged as a critical solution to enhance interpretability, ensuring that students and educators can understand, validate, and act upon AI-generated assessments. This chapter explores cutting-edge techniques for explainable AI in student feedback systems, including attention mechanisms in NLP, SHapley Additive exPlanations (SHAP), and Local Interpretable Model-agnostic Explanations (LIME). It also examines human-AI interaction, algorithmic authority, and ethical considerations in AI-driven assessments. Through case studies of personalized student evaluation platforms, this research highlights the practical implications of XAI in fostering transparency, engagement, and equity in learning environments. The findings underscore the necessity of integrating interpretable AI models that align with pedagogical frameworks, ensuring that AI serves as a collaborative tool rather than an autonomous decision-maker. By bridging the gap between AI interpretability and pedagogical decision-making, this work advances the development of ethical, transparent, and student-centric AI-driven feedback systems.

Introduction

The adoption of AI in education has transformed student assessment by enabling data-driven, scalable, and personalized feedback systems [1,2]. Traditional evaluation methods, often limited by time constraints and subjectivity, are being supplemented by AI-driven models capable of analyzing vast amounts of student data, including written assignments, quizzes, and engagement patterns [3]. These systems employ NLP and machine learning (ML) algorithms to generate real-time feedback, allowing students to identify learning gaps and educators to adjust instructional strategies accordingly [4-7]. Their efficiency, AI-driven feedback mechanisms often function as “black boxes,” where the reasoning behind assessment outcomes remains opaque to students and instructors. The lack of interpretability in these models raises concerns regarding fairness, accuracy, and trust, making explainability a critical aspect of AI-driven educational systems [8].

XAI addresses the challenges posed by opaque AI models by providing insights into the decision-making process of algorithms [9]. In the context of student feedback systems, XAI techniques, such as SHAP, LIME, and attention mechanisms, enable students and educators to understand how AI-generated feedback was formulated [10]. By highlighting key features that influence AI assessments, these methods ensure that feedback was transparent, interpretable, and aligned with pedagogical objectives [11,12]. The ability to trace AI-driven decisions allows educators to validate AI-generated feedback, ensuring that automated assessments do not inadvertently reinforce biases or provide misleading recommendations [13]. As educational institutions increasingly integrate AI into teaching and assessment frameworks, the need for interpretable AI models becomes more pronounced, ensuring that AI serves as an assistive tool rather than an unquestionable authority [14].

The interaction between AI and human educators plays a crucial role in fostering meaningful and explainable feedback systems [15]. While AI excels in processing large-scale data and identifying patterns, human instructors provide essential contextual understanding, emotional intelligence, and pedagogical expertise that AI lacks [16]. A collaborative approach, where AI-generated feedback was supplemented by human oversight, ensures that assessments remain accurate, fair, and aligned with curriculum goals. Educators must be equipped with the necessary AI literacy to interpret, critique, and refine AI-driven evaluations, ensuring that students receive comprehensive and contextually relevant feedback [17]. Students must be able to engage with AI-generated explanations, enabling them to understand not only their performance but also the reasoning behind the feedback receive. By integrating interactive explainability features into AI-driven learning platforms, students can actively participate in their educational progress, fostering a more transparent and student-centered learning environment [18].