Democratizing Foundations of Problem-Solving with AI: A Breadth-First Search Curriculum for Middle School Students
- URL: http://arxiv.org/abs/2604.01396v1
- Date: Wed, 01 Apr 2026 20:54:14 GMT
- Title: Democratizing Foundations of Problem-Solving with AI: A Breadth-First Search Curriculum for Middle School Students
- Abstract summary: This paper presents the design and implementation of an AI4K12-aligned curriculum that embeds AI learning goals within a rural middle school science classroom.<n>Students learned BFS as a strategy for exploring networks and identifying shortest paths, then applied it to science contexts involving virus spread and contact tracing.<n>Results suggest that students engaged productively with the curriculum, improved their understanding of BFS and AI problem-solving, and benefited from learning these ideas within ongoing science instruction.
- Score: 5.692320594711895
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: As AI becomes more common in students' everyday experiences, a major challenge for K-12 AI education is designing learning experiences that can be meaningfully integrated into existing subject-area instruction. This paper presents the design and implementation of an AI4K12-aligned curriculum that embeds AI learning goals within a rural middle school science classroom using Breadth-First Search (BFS) as an accessible entry point to AI problem-solving. Through unplugged activities and an interactive simulation environment, students learned BFS as a strategy for exploring networks and identifying shortest paths, then applied it to science contexts involving virus spread and contact tracing. To examine engagement and learning, we analyzed pre- and post-assessments, student work artifacts, and a teacher interview. Results suggest that students engaged productively with the curriculum, improved their understanding of BFS and AI problem-solving, and benefited from learning these ideas within ongoing science instruction. Teacher feedback further indicated that the module fit well within the science curriculum while supporting intended science learning outcomes. We conclude with curriculum and design considerations for broadening access to learning about problem-solving with AI in education.
Related papers
- Reshaping Undergraduate Computer Science Education in the Generative AI Era [85.78707817763434]
Generative AI represents a turning point for Computer Science (CS) education.<n>To keep pace with advances in AI technology, CS curricula should consider a shift toward understanding and verifying AI-generated artifacts.<n>We believe that to effectively prepare future computer science graduates, capable of creating, solving problems, and managing, as well as co-creating, artifacts with AI.
arXiv Detail & Related papers (2026-05-02T05:26:24Z) - Exploring Student Interactions with AI-Powered Learning Tools: A Qualitative Study Connecting Interaction Patterns to Educational Learning Theories [0.0]
This study focuses on how students engage with tools like ChatGPT, Grammarly, and Khan Academy.<n>We looked at four types of interaction directive, assistive, dialogic, and empathetic and compared them with learning approaches like behaviorism, cognitivism, constructivism, and humanism.<n>Our findings show that how useful an AI tool feels is not just about its features, but also about how students personally connect with it.
arXiv Detail & Related papers (2025-11-29T15:27:37Z) - "I Like That You Have to Poke Around": Instructors on How Experiential Approaches to AI Literacy Spark Inquiry and Critical Thinking [0.5872014229110213]
This paper presents findings from a study of AI User, a modular, web-based curriculum that teaches core AI concepts through interactive, no-code projects grounded in real-world scenarios.<n>Fifteen community college instructors participated in structured focus groups, completing the projects as learners and providing feedback through individual reflection and group discussion.<n>Findings highlight instructors' appreciation for exploratory tasks, role-based simulations, and real-world relevance, while also surfacing design trade-offs around cognitive load, guidance, and adaptability for diverse learners.
arXiv Detail & Related papers (2025-11-07T17:05:58Z) - Charting the Future of AI in Project-Based Learning: A Co-Design
Exploration with Students [35.05435052195561]
The increasing use of Artificial Intelligence (AI) by students in learning presents new challenges for assessing their learning outcomes.
This paper introduces a co-design study to explore the potential of students' AI usage data as a novel material for assessment.
arXiv Detail & Related papers (2024-01-26T14:49:29Z) - Understanding Teacher Perspectives and Experiences after Deployment of
AI Literacy Curriculum in Middle-school Classrooms [12.35885897302579]
We investigate the experiences of seven teachers following their implementation of modules from the MIT RAICA curriculum.
Our analysis suggests that the AI modules expanded our teachers' knowledge in the field.
Our teachers advocated their need for better external support when navigating technological resources.
arXiv Detail & Related papers (2023-12-08T05:36:16Z) - Transdisciplinary AI Education: The Confluence of Curricular and
Community Needs in the Instruction of Artificial Intelligence [0.7133676002283578]
We examine the current state of AI in education and explore the potential benefits and challenges of incorporating this technology into the classroom.
This paper delves into the AI program currently in development for Neom Community School and the larger Education, Research, and Innovation Sector in Neom, Saudi Arabia s new megacity under development.
arXiv Detail & Related papers (2023-11-10T17:26:27Z) - ActiveAI: Introducing AI Literacy for Middle School Learners with
Goal-based Scenario Learning [0.0]
The ActiveAI project addresses key challenges in AI education for grades 7-9 students.
The app incorporates a variety of learner inputs like sliders, steppers, and collectors to enhance understanding.
The project is currently in the implementation stage, leveraging the intelligent tutor design principles for app development.
arXiv Detail & Related papers (2023-08-21T11:43:43Z) - Learning to Prompt in the Classroom to Understand AI Limits: A pilot
study [35.06607166918901]
Large Language Models (LLM) and the derived chatbots, like ChatGPT, have highly improved the natural language processing capabilities of AI systems.
However, excitement has led to negative sentiments, even as AI methods demonstrate remarkable contributions.
A pilot educational intervention was performed in a high school with 21 students.
arXiv Detail & Related papers (2023-07-04T07:51:37Z) - AGI: Artificial General Intelligence for Education [41.45039606933712]
This position paper reviews artificial general intelligence (AGI)'s key concepts, capabilities, scope, and potential within future education.
It highlights that AGI can significantly improve intelligent tutoring systems, educational assessment, and evaluation procedures.
The paper emphasizes that AGI's capabilities extend to understanding human emotions and social interactions.
arXiv Detail & Related papers (2023-04-24T22:31:59Z) - Teaching Fairness, Accountability, Confidentiality, and Transparency in
Artificial Intelligence through the Lens of Reproducibility [38.87910190291545]
We explain the setup for a technical, graduate-level course on Fairness, Accountability, Confidentiality and Transparency in Artificial Intelligence (FACT-AI) at the University of Amsterdam.
The focal point of the course is a group project based on existing FACT-AI algorithms from top AI conferences, and writing a report about their experiences.
arXiv Detail & Related papers (2021-11-01T10:58:35Z) - Comparative Study of Learning Outcomes for Online Learning Platforms [47.5164159412965]
Personalization and active learning are key aspects to successful learning.
We run a comparative head-to-head study of learning outcomes for two popular online learning platforms.
arXiv Detail & Related papers (2021-04-15T20:40:24Z) - 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)
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.