Designing the Future of Entrepreneurship Education: Exploring an AI-Empowered Scaffold System for Business Plan Development
- URL: http://arxiv.org/abs/2505.23326v1
- Date: Thu, 29 May 2025 10:35:55 GMT
- Title: Designing the Future of Entrepreneurship Education: Exploring an AI-Empowered Scaffold System for Business Plan Development
- Authors: Junhua Zhu, Lan Luo,
- Abstract summary: Entrepreneurship education equips students to transform innovative ideas into actionable entrepreneurship plans.<n>Traditional approaches often struggle to provide the personalized guidance and practical alignment needed for success.<n>This study investigates the design needs for an AI-empowered scaffold system to address these challenges.
- Score: 2.240765873294129
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Entrepreneurship education equips students to transform innovative ideas into actionable entrepreneurship plans, yet traditional approaches often struggle to provide the personalized guidance and practical alignment needed for success. Focusing on the business plan as a key learning tool and evaluation method, this study investigates the design needs for an AI-empowered scaffold system to address these challenges. Based on qualitative insights from educators and students, the findings highlight three critical dimensions for system design: mastery of business plan development, alignment with entrepreneurial learning goals, and integration of adaptive system features. These findings underscore the transformative potential of AI in bridging gaps in entrepreneurship education while emphasizing the enduring value of human mentorship and experiential learning.
Related papers
- Form-Substance Discrimination: Concept, Cognition, and Pedagogy [55.2480439325792]
This paper examines form-substance discrimination as an essential learning outcome for curriculum development in higher education.<n>We propose practical strategies for fostering this ability through curriculum design, assessment practices, and explicit instruction.
arXiv Detail & Related papers (2025-04-01T04:15:56Z) - ARCHED: A Human-Centered Framework for Transparent, Responsible, and Collaborative AI-Assisted Instructional Design [10.99360129432492]
ARCHED is a framework that ensures human educators remain central in the design process while leveraging AI capabilities.<n>The framework integrates specialized AI agents - one generating diverse pedagogical options and another evaluating alignment with learning objectives.<n> Empirical evaluations demonstrate that ARCHED enhances instructional design quality while preserving educator oversight, marking a step forward in responsible AI integration in education.
arXiv Detail & Related papers (2025-03-11T22:19:46Z) - Mentoring Software in Education and Its Impact on Teacher Development: An Integrative Literature Review [0.0]
This study explores the transformative potential of digital mentoring platforms.<n>The research synthesizes findings from existing literature to assess the effectiveness of key features.<n>Financial constraints, limited institutional support, and data privacy concerns remain significant challenges.
arXiv Detail & Related papers (2025-02-18T04:01:45Z) - Scaffolding Creativity: Integrating Generative AI Tools and Real-world Experiences in Business Education [0.0]
This case study explores how AI-assisted learning, combined with experiential components, impacts students' creative processes and learning outcomes.<n>Our findings reveal that this integrated approach accelerates knowledge acquisition, enables students to overcome traditional creative barriers, and facilitates a dynamic interplay between AI-generated insights and real-world observations.
arXiv Detail & Related papers (2025-01-11T12:31:10Z) - Advancing Transformative Education: Generative AI as a Catalyst for Equity and Innovation [0.0]
Generative AI is transforming education by enabling personalized learning, enhancing administrative efficiency, and fostering creative engagement.
This paper explores the opportunities and challenges these tools bring to pedagogy, proposing actionable frameworks to address existing equity gaps.
arXiv Detail & Related papers (2024-11-24T19:53:48Z) - Education in the Era of Neurosymbolic AI [0.6468510459310326]
We propose a system that leverages the unique affordances of pedagogical agents as critical components of a hybrid NAI architecture.
We conclude that education in the era of NAI will make learning more accessible, equitable, and aligned with real-world skills.
arXiv Detail & Related papers (2024-11-16T19:18:39Z) - From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents [78.15899922698631]
MAIC (Massive AI-empowered Course) is a new form of online education that leverages LLM-driven multi-agent systems to construct an AI-augmented classroom.
We conduct preliminary experiments at Tsinghua University, one of China's leading universities.
arXiv Detail & Related papers (2024-09-05T13:22:51Z) - Integrating AI and Learning Analytics for Data-Driven Pedagogical Decisions and Personalized Interventions in Education [0.2812395851874055]
This research study explores the conceptualization, development, and deployment of an innovative learning analytics tool.
By analyzing critical data points such as students' stress levels, curiosity, confusion, agitation, topic preferences, and study methods, the tool provides a comprehensive view of the learning environment.
This research underscores AI's role in shaping personalized, data-driven education.
arXiv Detail & Related papers (2023-12-15T06:00:26Z) - Towards Goal-oriented Intelligent Tutoring Systems in Online Education [69.06930979754627]
We propose a new task, named Goal-oriented Intelligent Tutoring Systems (GITS)
GITS aims to enable the student's mastery of a designated concept by strategically planning a customized sequence of exercises and assessment.
We propose a novel graph-based reinforcement learning framework, named Planning-Assessment-Interaction (PAI)
arXiv Detail & Related papers (2023-12-03T12:37:16Z) - Modelling Assessment Rubrics through Bayesian Networks: a Pragmatic Approach [40.06500618820166]
This paper presents an approach to deriving a learner model directly from an assessment rubric.
We illustrate how the approach can be applied to automatize the human assessment of an activity developed for testing computational thinking skills.
arXiv Detail & Related papers (2022-09-07T10:09:12Z) - Procedure Planning in Instructional Videosvia Contextual Modeling and
Model-based Policy Learning [114.1830997893756]
This work focuses on learning a model to plan goal-directed actions in real-life videos.
We propose novel algorithms to model human behaviors through Bayesian Inference and model-based Imitation Learning.
arXiv Detail & Related papers (2021-10-05T01:06:53Z) - Design principles for a hybrid intelligence decision support system for
business model validation [4.127347156839169]
This paper develops design principles for a Hybrid Intelligence decision support system (HI-DSS)
We follow a design science research approach to design a prototype artifact and a set of design principles.
Our study provides prescriptive knowledge for HI-DSS and contributes to previous work on decision support for business models, the applications of complementary strengths of humans and machines for making decisions, and support systems for extremely uncertain decision-making problems.
arXiv Detail & Related papers (2021-05-07T16:13:36Z) - 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) - Distributed and Democratized Learning: Philosophy and Research
Challenges [80.39805582015133]
We propose a novel design philosophy called democratized learning (Dem-AI)
Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently.
We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
arXiv Detail & Related papers (2020-03-18T08:45:10Z)
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.