Intelligent Tutors for Adult Learners: An Analysis of Needs and Challenges
- URL: http://arxiv.org/abs/2412.04477v3
- Date: Wed, 19 Feb 2025 03:08:14 GMT
- Title: Intelligent Tutors for Adult Learners: An Analysis of Needs and Challenges
- Authors: Adit Gupta, Momin Siddiqui, Glen Smith, Jenn Reddig, Christopher MacLellan,
- Abstract summary: This work examines the sociotechnical factors that influence the adoption and usage of intelligent tutoring systems in self-directed learning contexts.
We present Apprentice Tutors, a novel intelligent tutoring system designed to address the unique needs of adult learners.
- Score: 0.4916390772672078
- License:
- Abstract: This work examines the sociotechnical factors that influence the adoption and usage of intelligent tutoring systems in self-directed learning contexts, focusing specifically on adult learners. The study is divided into two parts. First, we present Apprentice Tutors, a novel intelligent tutoring system designed to address the unique needs of adult learners. The platform includes adaptive problem selection, real-time feedback, and visual dashboards to support learning in college algebra topics. Second, we investigate the specific needs and experiences of adult users through a deployment study and a series of focus groups. Using thematic analysis, we identify key challenges and opportunities to improve tutor design and adoption. Based on these findings, we offer actionable design recommendations to help developers create intelligent tutoring systems that better align with the motivations and learning preferences of adult learners. This work contributes to a wider understanding of how to improve educational technologies to support lifelong learning and professional development.
Related papers
- LLM-powered Multi-agent Framework for Goal-oriented Learning in Intelligent Tutoring System [54.71619734800526]
GenMentor is a multi-agent framework designed to deliver goal-oriented, personalized learning within ITS.
It maps learners' goals to required skills using a fine-tuned LLM trained on a custom goal-to-skill dataset.
GenMentor tailors learning content with an exploration-drafting-integration mechanism to align with individual learner needs.
arXiv Detail & Related papers (2025-01-27T03:29:44Z) - Scaffolding Language Learning via Multi-modal Tutoring Systems with Pedagogical Instructions [34.760230622675365]
Intelligent tutoring systems (ITSs) imitate human tutors and aim to provide customized instructions or feedback to learners.
With the emergence of generative artificial intelligence, large language models (LLMs) entitle the systems to complex and coherent conversational interactions.
We investigate how pedagogical instructions facilitate the scaffolding in ITSs, by conducting a case study on guiding children to describe images for language learning.
arXiv Detail & Related papers (2024-04-04T13:22:28Z) - YODA: Teacher-Student Progressive Learning for Language Models [82.0172215948963]
This paper introduces YODA, a teacher-student progressive learning framework.
It emulates the teacher-student education process to improve the efficacy of model fine-tuning.
Experiments show that training LLaMA2 with data from YODA improves SFT with significant performance gain.
arXiv Detail & Related papers (2024-01-28T14:32:15Z) - Anticipating User Needs: Insights from Design Fiction on Conversational Agents for Computational Thinking [10.363782876965221]
We envision a conversational agent that guides students stepwise through exercises, tuning its method of guidance with an awareness of the educational background, skills and deficits, and learning preferences.
The insights obtained in this paper can guide future implementations of tutoring agents oriented toward teaching computational thinking and computer programming.
arXiv Detail & Related papers (2023-11-12T16:19:03Z) - Reinforcement Learning Tutor Better Supported Lower Performers in a Math
Task [32.6507926764587]
Reinforcement learning could be a key tool to reduce the development cost and improve the effectiveness of intelligent tutoring software.
We show that deep reinforcement learning can be used to provide adaptive pedagogical support to students learning about the concept of volume.
arXiv Detail & Related papers (2023-04-11T02:11:24Z) - Teachable Reinforcement Learning via Advice Distillation [161.43457947665073]
We propose a new supervision paradigm for interactive learning based on "teachable" decision-making systems that learn from structured advice provided by an external teacher.
We show that agents that learn from advice can acquire new skills with significantly less human supervision than standard reinforcement learning algorithms.
arXiv Detail & Related papers (2022-03-19T03:22:57Z) - Analyzing Adaptive Scaffolds that Help Students Develop Self-Regulated
Learning Behaviors [6.075903612065429]
This paper presents a systematic framework for adaptive scaffolding in Betty's Brain.
Students construct a causal model to teach a virtual agent, generically named Betty.
We analyze the impact of adaptive scaffolds on students' learning behaviors and performance.
arXiv Detail & Related papers (2022-02-20T00:02:31Z) - Personalized Education in the AI Era: What to Expect Next? [76.37000521334585]
The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to meet her desired goal.
In recent years, the boost of artificial intelligence (AI) and machine learning (ML) has unfolded novel perspectives to enhance personalized education.
arXiv Detail & Related papers (2021-01-19T12:23:32Z) - Dual Policy Distillation [58.43610940026261]
Policy distillation, which transfers a teacher policy to a student policy, has achieved great success in challenging tasks of deep reinforcement learning.
In this work, we introduce dual policy distillation(DPD), a student-student framework in which two learners operate on the same environment to explore different perspectives of the environment.
The key challenge in developing this dual learning framework is to identify the beneficial knowledge from the peer learner for contemporary learning-based reinforcement learning algorithms.
arXiv Detail & Related papers (2020-06-07T06:49:47Z) - Neural Multi-Task Learning for Teacher Question Detection in Online
Classrooms [50.19997675066203]
We build an end-to-end neural framework that automatically detects questions from teachers' audio recordings.
By incorporating multi-task learning techniques, we are able to strengthen the understanding of semantic relations among different types of questions.
arXiv Detail & Related papers (2020-05-16T02:17:04Z)
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.