Towards Applying Powerful Large AI Models in Classroom Teaching:
Opportunities, Challenges and Prospects
- URL: http://arxiv.org/abs/2305.03433v2
- Date: Mon, 12 Jun 2023 11:53:37 GMT
- Title: Towards Applying Powerful Large AI Models in Classroom Teaching:
Opportunities, Challenges and Prospects
- Authors: Kehui Tan, Tianqi Pang, Chenyou Fan and Song Yu
- Abstract summary: This perspective paper proposes a series of interactive scenarios that utilize Artificial Intelligence (AI) to enhance classroom teaching.
We explore the potential of AI to augment and enrich teacher-student dialogues and improve the quality of teaching.
- Score: 5.457842083043013
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This perspective paper proposes a series of interactive scenarios that
utilize Artificial Intelligence (AI) to enhance classroom teaching, such as
dialogue auto-completion, knowledge and style transfer, and assessment of
AI-generated content. By leveraging recent developments in Large Language
Models (LLMs), we explore the potential of AI to augment and enrich
teacher-student dialogues and improve the quality of teaching. Our goal is to
produce innovative and meaningful conversations between teachers and students,
create standards for evaluation, and improve the efficacy of AI-for-Education
initiatives. In Section 3, we discuss the challenges of utilizing existing LLMs
to effectively complete the educated tasks and present a unified framework for
addressing diverse education dataset, processing lengthy conversations, and
condensing information to better accomplish more downstream tasks. In Section
4, we summarize the pivoting tasks including Teacher-Student Dialogue
Auto-Completion, Expert Teaching Knowledge and Style Transfer, and Assessment
of AI-Generated Content (AIGC), providing a clear path for future research. In
Section 5, we also explore the use of external and adjustable LLMs to improve
the generated content through human-in-the-loop supervision and reinforcement
learning. Ultimately, this paper seeks to highlight the potential for AI to aid
the field of education and promote its further exploration.
Related papers
- Roadmap towards Superhuman Speech Understanding using Large Language Models [60.57947401837938]
Large language models (LLMs) integrate speech and audio data.
Recent advances, such as GPT-4o, highlight the potential for end-to-end speech LLMs.
We propose a five-level roadmap, ranging from basic automatic speech recognition (ASR) to advanced superhuman models.
arXiv Detail & Related papers (2024-10-17T06:44:06Z) - Exploring Knowledge Tracing in Tutor-Student Dialogues [53.52699766206808]
We present a first attempt at performing knowledge tracing (KT) in tutor-student dialogues.
We propose methods to identify the knowledge components/skills involved in each dialogue turn.
We then apply a range of KT methods on the resulting labeled data to track student knowledge levels over an entire dialogue.
arXiv Detail & Related papers (2024-09-24T22:31:39Z) - SPL: A Socratic Playground for Learning Powered by Large Language Model [5.383689446227398]
Socratic Playground for Learning (SPL) is a dialogue-based ITS powered by the GPT-4 model.
SPL aims to enhance personalized and adaptive learning experiences tailored to individual needs.
arXiv Detail & Related papers (2024-06-20T01:18:52Z) - LOVA3: Learning to Visual Question Answering, Asking and Assessment [61.51687164769517]
Question answering, asking, and assessment are three innate human traits crucial for understanding the world and acquiring knowledge.
Current Multimodal Large Language Models (MLLMs) primarily focus on question answering, often neglecting the full potential of questioning and assessment skills.
We introduce LOVA3, an innovative framework named "Learning tO Visual question Answering, Asking and Assessment"
arXiv Detail & Related papers (2024-05-23T18:21:59Z) - Large Language Models for Education: A Survey and Outlook [69.02214694865229]
We systematically review the technological advancements in each perspective, organize related datasets and benchmarks, and identify the risks and challenges associated with deploying LLMs in education.
Our survey aims to provide a comprehensive technological picture for educators, researchers, and policymakers to harness the power of LLMs to revolutionize educational practices and foster a more effective personalized learning environment.
arXiv Detail & Related papers (2024-03-26T21:04:29Z) - Enhancing Instructional Quality: Leveraging Computer-Assisted Textual
Analysis to Generate In-Depth Insights from Educational Artifacts [13.617709093240231]
We examine how artificial intelligence (AI) and machine learning (ML) methods can analyze educational content, teacher discourse, and student responses to foster instructional improvement.
We identify key areas where AI/ML integration offers significant advantages, including teacher coaching, student support, and content development.
This paper emphasizes the importance of aligning AI/ML technologies with pedagogical goals to realize their full potential in educational settings.
arXiv Detail & Related papers (2024-03-06T18:29:18Z) - Bringing Generative AI to Adaptive Learning in Education [58.690250000579496]
We shed light on the intersectional studies of generative AI and adaptive learning.
We argue that this union will contribute significantly to the development of the next-stage learning format in education.
arXiv Detail & Related papers (2024-02-02T23:54:51Z) - Generative AI and Its Educational Implications [0.0]
We discuss the implications of generative AI on education across four critical sections.
We propose ways in which generative AI can transform the educational landscape.
Acknowledging the societal impact, we emphasize the need for updating curricula.
arXiv Detail & Related papers (2023-12-26T21:29:31Z) - New Era of Artificial Intelligence in Education: Towards a Sustainable
Multifaceted Revolution [2.94944680995069]
ChatGPT's high performance on standardized academic tests has thrust the topic of artificial intelligence (AI) into the mainstream conversation about the future of education.
This research aims to investigate the potential impact of AI on education through review and analysis of the existing literature across three major axes: applications, advantages, and challenges.
arXiv Detail & Related papers (2023-05-12T08:22:54Z) - Practical and Ethical Challenges of Large Language Models in Education:
A Systematic Scoping Review [5.329514340780243]
Large language models (LLMs) have the potential to automate the laborious process of generating and analysing textual content.
There are concerns regarding the practicality and ethicality of these innovations.
We conducted a systematic scoping review of 118 peer-reviewed papers published since 2017 to pinpoint the current state of research.
arXiv Detail & Related papers (2023-03-17T18:14:46Z) - Opportunities and Challenges in Neural Dialog Tutoring [54.07241332881601]
We rigorously analyze various generative language models on two dialog tutoring datasets for language learning.
We find that although current approaches can model tutoring in constrained learning scenarios, they perform poorly in less constrained scenarios.
Our human quality evaluation shows that both models and ground-truth annotations exhibit low performance in terms of equitable tutoring.
arXiv Detail & Related papers (2023-01-24T11:00:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.