içgörü - Computer Science - # Interactive Feedback Generation for Student Essay Evaluation

Contestable AI Framework for Interactive Feedback in Evaluating Student Essays

Q: How can CAELF's formal reasoning and multi-agent argumentation be extended to other educational applications beyond essay evaluation, such as programming assignments or mathematical problem-solving?

CAELF's formal reasoning and multi-agent argumentation framework can be effectively adapted to various educational applications, including programming assignments and mathematical problem-solving. In programming education, CAELF could utilize multiple Teaching-Assistant Agents (TA Agents) that specialize in different programming paradigms or languages. Each TA Agent could evaluate code submissions based on criteria such as correctness, efficiency, readability, and adherence to best practices. The multi-agent system would allow these agents to engage in discussions about the strengths and weaknesses of the code, generating a comprehensive feedback report that highlights areas for improvement. For mathematical problem-solving, CAELF could implement a similar approach by having TA Agents focus on different aspects of mathematical reasoning, such as problem formulation, solution strategies, and justification of answers. The agents could debate the validity of various approaches to a problem, allowing for a rich exploration of mathematical concepts. By integrating formal reasoning, CAELF could ensure that the feedback provided is not only accurate but also pedagogically sound, guiding students through the reasoning process and helping them understand the underlying principles. In both cases, the interactive feedback mechanism would enable students to challenge the evaluations, fostering a deeper understanding of the subject matter. This adaptability of CAELF to diverse educational contexts underscores its potential to enhance learning outcomes across various domains.

Q: What are the potential limitations of CAELF's approach, and how can it be further improved to address issues like AI-driven cheating or the alignment of student submissions with reliable knowledge?

While CAELF presents a promising framework for interactive feedback, several limitations must be addressed to enhance its effectiveness. One significant concern is the potential for AI-driven cheating, where students might exploit the system by embedding prompts or manipulating inputs to receive favorable evaluations. To mitigate this risk, CAELF could incorporate mechanisms for detecting unusual patterns in submissions, such as sudden changes in writing style or content that do not align with a student's previous work. Implementing plagiarism detection algorithms and requiring students to provide justifications for their submissions could further deter dishonest practices. Another limitation is the alignment of student submissions with reliable knowledge. CAELF's effectiveness relies on the assumption that the underlying LLM possesses accurate and consistent knowledge. To improve this aspect, future iterations of CAELF could integrate external knowledge bases or utilize retrieval-augmented generation (RAG) techniques to ensure that the feedback is grounded in verified information. By cross-referencing student submissions with authoritative sources, CAELF can enhance the reliability of its evaluations and reduce the likelihood of propagating misinformation. Additionally, continuous training and updating of the LLM with current educational standards and practices will help maintain the relevance and accuracy of the feedback provided. By addressing these limitations, CAELF can become a more robust tool for educational assessment and feedback.

Q: Given the importance of interactive feedback in fostering reflective learning, how can CAELF's principles be applied to enhance interactive learning experiences in other domains, such as professional training or lifelong learning?

CAELF's principles of interactive feedback and multi-agent argumentation can significantly enhance interactive learning experiences in domains such as professional training and lifelong learning. In professional training, CAELF can be adapted to simulate real-world scenarios where trainees must apply their knowledge and skills. For instance, in fields like healthcare or engineering, CAELF could facilitate role-playing exercises where trainees receive feedback from multiple agents representing different stakeholders (e.g., patients, clients, or colleagues). This approach would encourage critical thinking and reflection on the implications of their decisions, fostering a deeper understanding of the complexities involved in their professions. In the context of lifelong learning, CAELF can support adult learners by providing personalized feedback on various learning activities, such as project proposals, presentations, or skill assessments. The interactive nature of CAELF allows learners to engage in dialogue with the system, challenging feedback and seeking clarification on specific points. This iterative process promotes self-regulation and encourages learners to take ownership of their learning journey. Moreover, CAELF's formal reasoning capabilities can be utilized to create tailored learning paths based on individual progress and performance. By analyzing the arguments and feedback generated during interactions, CAELF can identify knowledge gaps and suggest targeted resources or activities to address them. This personalized approach not only enhances the learning experience but also empowers learners to pursue their interests and goals more effectively. Overall, by applying CAELF's principles to professional training and lifelong learning, educational institutions and organizations can create more engaging, reflective, and effective learning environments that cater to the diverse needs of learners.

Temel Kavramlar

A Contestable AI Empowered LLM Framework (CAELF) that integrates computational argumentation to automate interactive feedback, enhancing the reasoning and interaction capabilities of language models in educational settings.

Özet

The paper introduces CAELF, a Contestable AI Empowered LLM Framework for automating interactive feedback in evaluating student essays. CAELF employs a multi-agent argumentation system to address the limitations of existing LLMs in providing effective interactive feedback.

The key aspects of CAELF are:

LLM Discussion: Multiple Teaching-Assistant (TA) agents discuss the essay based on assessment rubrics, forming arguments.
Formal Reasoning for Feedback Generation: The Teacher agent analyzes the arguments through a formal reasoning process using computational argumentation. It then provides a grade and summative feedback for the essay.
Interaction with User: Students can challenge the feedback or grade, initiating a new round of discussion and feedback generation with additional inputs from the student.

The paper presents a case study on evaluating critical thinking essays, where CAELF demonstrates:

Initial grading accuracy comparable to GPT-4 across all four dimensions of the assessment rubric.
Significantly better performance in interaction grading accuracy and maintaining consistent evaluations despite user challenges.
In a human user study, CAELF's feedback outperformed baselines in terms of factual accuracy, self-regulation, and suggestions for future improvement.

The authors highlight that CAELF's integration of formal reasoning and multi-agent argumentation effectively addresses the weaknesses of existing LLMs in providing interactive feedback, offering a promising solution to overcome the time and resource barriers that have limited the adoption of interactive feedback in educational settings.

Özeti Özelleştir

Yapay Zeka ile Yeniden Yaz

Alıntıları Oluştur

Kaynağı Çevir

Başka Bir Dile

Zihin Haritası Oluştur

kaynak içeriğinden

Kaynak

arxiv.org

İstatistikler

Feedback is one of the most powerful influences on learning and achievement.
Interactive feedback, where feedback flows in both directions between teacher and student, is more effective than traditional one-way feedback.
Large Language Models (LLMs) have potential for automating feedback, but struggle with reasoning and interaction in an interactive setting.

Alıntılar

"Feedback is one of the most powerful influences on learning and achievement."
"Interactive feedback should not merely be a one-way transmission of information from instructor to student. Instead, it should involve students actively engaging with the feedback, interpreting it, and using it as a basis for further learning and improvement."

Önemli Bilgiler Şuradan Elde Edildi

"My Grade is Wrong!": A Contestable AI Framework for Interactive Feedback in Evaluating Student Essays

by Shengxin Hon... : arxiv.org 09-12-2024

https://arxiv.org/pdf/2409.07453.pdf

"My Grade is Wrong!": A Contestable AI Framework for Interactive Feedback in Evaluating Student Essays

Daha Derin Sorular

How can CAELF's formal reasoning and multi-agent argumentation be extended to other educational applications beyond essay evaluation, such as programming assignments or mathematical problem-solving?

CAELF's formal reasoning and multi-agent argumentation framework can be effectively adapted to various educational applications, including programming assignments and mathematical problem-solving. In programming education, CAELF could utilize multiple Teaching-Assistant Agents (TA Agents) that specialize in different programming paradigms or languages. Each TA Agent could evaluate code submissions based on criteria such as correctness, efficiency, readability, and adherence to best practices. The multi-agent system would allow these agents to engage in discussions about the strengths and weaknesses of the code, generating a comprehensive feedback report that highlights areas for improvement.
For mathematical problem-solving, CAELF could implement a similar approach by having TA Agents focus on different aspects of mathematical reasoning, such as problem formulation, solution strategies, and justification of answers. The agents could debate the validity of various approaches to a problem, allowing for a rich exploration of mathematical concepts. By integrating formal reasoning, CAELF could ensure that the feedback provided is not only accurate but also pedagogically sound, guiding students through the reasoning process and helping them understand the underlying principles.
In both cases, the interactive feedback mechanism would enable students to challenge the evaluations, fostering a deeper understanding of the subject matter. This adaptability of CAELF to diverse educational contexts underscores its potential to enhance learning outcomes across various domains.

What are the potential limitations of CAELF's approach, and how can it be further improved to address issues like AI-driven cheating or the alignment of student submissions with reliable knowledge?

While CAELF presents a promising framework for interactive feedback, several limitations must be addressed to enhance its effectiveness. One significant concern is the potential for AI-driven cheating, where students might exploit the system by embedding prompts or manipulating inputs to receive favorable evaluations. To mitigate this risk, CAELF could incorporate mechanisms for detecting unusual patterns in submissions, such as sudden changes in writing style or content that do not align with a student's previous work. Implementing plagiarism detection algorithms and requiring students to provide justifications for their submissions could further deter dishonest practices.
Another limitation is the alignment of student submissions with reliable knowledge. CAELF's effectiveness relies on the assumption that the underlying LLM possesses accurate and consistent knowledge. To improve this aspect, future iterations of CAELF could integrate external knowledge bases or utilize retrieval-augmented generation (RAG) techniques to ensure that the feedback is grounded in verified information. By cross-referencing student submissions with authoritative sources, CAELF can enhance the reliability of its evaluations and reduce the likelihood of propagating misinformation.
Additionally, continuous training and updating of the LLM with current educational standards and practices will help maintain the relevance and accuracy of the feedback provided. By addressing these limitations, CAELF can become a more robust tool for educational assessment and feedback.

Given the importance of interactive feedback in fostering reflective learning, how can CAELF's principles be applied to enhance interactive learning experiences in other domains, such as professional training or lifelong learning?

CAELF's principles of interactive feedback and multi-agent argumentation can significantly enhance interactive learning experiences in domains such as professional training and lifelong learning. In professional training, CAELF can be adapted to simulate real-world scenarios where trainees must apply their knowledge and skills. For instance, in fields like healthcare or engineering, CAELF could facilitate role-playing exercises where trainees receive feedback from multiple agents representing different stakeholders (e.g., patients, clients, or colleagues). This approach would encourage critical thinking and reflection on the implications of their decisions, fostering a deeper understanding of the complexities involved in their professions.
In the context of lifelong learning, CAELF can support adult learners by providing personalized feedback on various learning activities, such as project proposals, presentations, or skill assessments. The interactive nature of CAELF allows learners to engage in dialogue with the system, challenging feedback and seeking clarification on specific points. This iterative process promotes self-regulation and encourages learners to take ownership of their learning journey.
Moreover, CAELF's formal reasoning capabilities can be utilized to create tailored learning paths based on individual progress and performance. By analyzing the arguments and feedback generated during interactions, CAELF can identify knowledge gaps and suggest targeted resources or activities to address them. This personalized approach not only enhances the learning experience but also empowers learners to pursue their interests and goals more effectively.
Overall, by applying CAELF's principles to professional training and lifelong learning, educational institutions and organizations can create more engaging, reflective, and effective learning environments that cater to the diverse needs of learners.