Scholarlink

Personalized adaptive assessment has emerged as a cornerstone of modern educational technology, aiming to provide learners with tailored evaluation pathways that evolve in response to their performance, behaviors, and contextual cues. While existing research has made progress using isolated techniques—such as automatic question generation, reinforcement learning–based item selection, or NLP-driven scoring—current systems remain fragmented, limited in transparency, and insufficiently sensitive to human factors. This paper presents a unified conceptual framework that integrates five complementary paradigms: (1) Natural Language Processing for open-response understanding, (2) Reinforcement Learning for dynamic item sequencing, (3) Adversarial Response Modeling for robustness against guessing and generative-AI misuse, (4) Emotion-Aware Modeling to adapt assessments based on affective states, and (5) Knowledge Graph–Driven Concept Routing to ensure pedagogical coherence. We additionally introduce a multi-agent architecture in which specialized agents coordinate to deliver adaptive question selection, emotion monitoring, explanation generation, and bias-aware scoring. Drawing from established findings in adaptive testing, cognitive diagnostics, fairness in automated assessment, and intelligent tutoring systems, this work articulates a research agenda focused on transparency, explainability, fairness, and real-time adaptation. The proposed framework is conceptual yet technically grounded, supported with formal equations for RL-based item selection, formal architectural schematics, and illustrative examples that mirror real classroom use cases. By unifying emotion-aware analytics, multi-agent coordination, and adversarial robustness, the paper aims to lay the foundation for a next-generation assessment paradigm that is human-centric, pedagogically aligned, and resilient to emerging forms of misuse.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Conceptual Integrated AI Framework and Research Agenda for Personalized Adaptive Assessment Using NLP, Reinforcement Learning, and Adversarial Response Modeling

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Tahmina Akter Anondi,
  • Ezekiel Nwokolo

摘要

Personalized adaptive assessment has emerged as a cornerstone of modern educational technology, aiming to provide learners with tailored evaluation pathways that evolve in response to their performance, behaviors, and contextual cues. While existing research has made progress using isolated techniques—such as automatic question generation, reinforcement learning–based item selection, or NLP-driven scoring—current systems remain fragmented, limited in transparency, and insufficiently sensitive to human factors. This paper presents a unified conceptual framework that integrates five complementary paradigms: (1) Natural Language Processing for open-response understanding, (2) Reinforcement Learning for dynamic item sequencing, (3) Adversarial Response Modeling for robustness against guessing and generative-AI misuse, (4) Emotion-Aware Modeling to adapt assessments based on affective states, and (5) Knowledge Graph–Driven Concept Routing to ensure pedagogical coherence. We additionally introduce a multi-agent architecture in which specialized agents coordinate to deliver adaptive question selection, emotion monitoring, explanation generation, and bias-aware scoring. Drawing from established findings in adaptive testing, cognitive diagnostics, fairness in automated assessment, and intelligent tutoring systems, this work articulates a research agenda focused on transparency, explainability, fairness, and real-time adaptation. The proposed framework is conceptual yet technically grounded, supported with formal equations for RL-based item selection, formal architectural schematics, and illustrative examples that mirror real classroom use cases. By unifying emotion-aware analytics, multi-agent coordination, and adversarial robustness, the paper aims to lay the foundation for a next-generation assessment paradigm that is human-centric, pedagogically aligned, and resilient to emerging forms of misuse.