Teacher-AI Collaboration for Curating and Customizing Lesson Plans in Low-Resource Schools
- URL: http://arxiv.org/abs/2507.00456v1
- Date: Tue, 01 Jul 2025 06:14:25 GMT
- Title: Teacher-AI Collaboration for Curating and Customizing Lesson Plans in Low-Resource Schools
- Authors: Deepak Varuvel Dennison, Bakhtawar Ahtisham, Kavyansh Chourasia, Nirmit Arora, Rahul Singh, Rene F. Kizilcec, Akshay Nambi, Tanuja Ganu, Aditya Vashistha,
- Abstract summary: This study investigates Shiksha copilot, an AI-assisted lesson planning tool deployed in government schools across Karnataka, India.<n>The system combined LLMs and human expertise through a structured process in which English and Kannada lesson plans were co-created by curators and AI.<n>We examine how educators collaborate with AI to generate context-sensitive lesson plans, assess the quality of AI-generated content, and analyze shifts in teaching practices.
- Score: 11.035761502788727
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: This study investigates Shiksha copilot, an AI-assisted lesson planning tool deployed in government schools across Karnataka, India. The system combined LLMs and human expertise through a structured process in which English and Kannada lesson plans were co-created by curators and AI; teachers then further customized these curated plans for their classrooms using their own expertise alongside AI support. Drawing on a large-scale mixed-methods study involving 1,043 teachers and 23 curators, we examine how educators collaborate with AI to generate context-sensitive lesson plans, assess the quality of AI-generated content, and analyze shifts in teaching practices within multilingual, low-resource environments. Our findings show that teachers used Shiksha copilot both to meet administrative documentation needs and to support their teaching. The tool eased bureaucratic workload, reduced lesson planning time, and lowered teaching-related stress, while promoting a shift toward activity-based pedagogy. However, systemic challenges such as staffing shortages and administrative demands constrained broader pedagogical change. We frame these findings through the lenses of teacher-AI collaboration and communities of practice to examine the effective integration of AI tools in teaching. Finally, we propose design directions for future teacher-centered EdTech, particularly in multilingual and Global South contexts.
Related papers
- The Impact of Large Language Models on K-12 Education in Rural India: A Thematic Analysis of Student Volunteer's Perspectives [1.7328324005163935]
AI-driven education has the potential to address learning disparities in rural schools.<n>This study examines the perceptions of volunteer teachers on AI integration in rural education.
arXiv Detail & Related papers (2025-05-06T04:14:32Z) - Connecting Feedback to Choice: Understanding Educator Preferences in GenAI vs. Human-Created Lesson Plans in K-12 Education -- A Comparative Analysis [11.204345070162592]
generative AI (GenAI) models are increasingly explored for educational applications.<n>This study compares lesson plans authored by human curriculum designers, a fine-tuned LLaMA-2-13b model trained on K-12 content, and a customized GPT-4 model.<n>Using a large-scale preference study with K-12 math educators, we examine how preferences vary across grade levels and instructional components.
arXiv Detail & Related papers (2025-04-07T19:28:19Z) - Transforming Teacher Education in Developing Countries: The Role of Generative AI in Bridging Theory and Practice [0.7416846035207727]
The study focuses on Ghana, where challenges such as limited pedagogical modeling, performance-based assessments, and practitioner-expertise gaps hinder progress.
GenAI has the capacity to address these issues by supporting content knowledge acquisition, a role that currently dominates teacher education programs.
The study concludes by recommending empirical research to explore these roles further and develop practical steps for integrating GenAI into teacher education systems responsibly and effectively.
arXiv Detail & Related papers (2024-11-16T06:46:09Z) - Representational Alignment Supports Effective Machine Teaching [81.19197059407121]
GRADE is a new controlled experimental setting to study pedagogy and representational alignment.<n>We find that improved representational alignment with a student improves student learning outcomes.<n>However, this effect is moderated by the size and representational diversity of the class being taught.
arXiv Detail & Related papers (2024-06-06T17:48:24Z) - Exploring Teachers' Perception of Artificial Intelligence: The Socio-emotional Deficiency as Opportunities and Challenges in Human-AI Complementarity in K-12 Education [1.9797215742507548]
In schools, teachers play a multitude of roles, serving as educators, counselors, decision-makers, and members of the school community.
With recent advances in artificial intelligence (AI), there is increasing discussion about how AI can assist, complement, and collaborate with teachers.
Our study seeks educators' perspectives on the potential strengths and limitations of AI across a spectrum of responsibilities.
arXiv Detail & Related papers (2024-05-20T15:43:04Z) - 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) - PapagAI:Automated Feedback for Reflective Essays [48.4434976446053]
We present the first open-source automated feedback tool based on didactic theory and implemented as a hybrid AI system.
The main objective of our work is to enable better learning outcomes for students and to complement the teaching activities of lecturers.
arXiv Detail & Related papers (2023-07-10T11:05:51Z) - An Experience Report of Executive-Level Artificial Intelligence
Education in the United Arab Emirates [53.04281982845422]
We present an experience report of teaching an AI course to business executives in the United Arab Emirates (UAE)
Rather than focusing only on theoretical and technical aspects, we developed a course that teaches AI with a view to enabling students to understand how to incorporate it into existing business processes.
arXiv Detail & Related papers (2022-02-02T20:59:53Z) - Iterative Teacher-Aware Learning [136.05341445369265]
In human pedagogy, teachers and students can interact adaptively to maximize communication efficiency.
We propose a gradient optimization based teacher-aware learner who can incorporate teacher's cooperative intention into the likelihood function.
arXiv Detail & Related papers (2021-10-01T00:27:47Z) - Creation and Evaluation of a Pre-tertiary Artificial Intelligence (AI)
Curriculum [58.86139968005518]
The Chinese University of Hong Kong (CUHK)-Jockey Club AI for the Future Project (AI4Future) co-created an AI curriculum for pre-tertiary education.
A team of 14 professors with expertise in engineering and education collaborated with 17 principals and teachers from 6 secondary schools to co-create the curriculum.
The co-creation process generated a variety of resources which enhanced the teachers knowledge in AI, as well as fostered teachers autonomy in bringing the subject matter into their classrooms.
arXiv Detail & Related papers (2021-01-19T11:26:19Z) - 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.