From chalkboards to chatbots: SELAR assists teachers in embracing AI in the curriculum
Project Overview
The document outlines the SELAR framework, which aims to help educators effectively incorporate generative AI into educational curricula, highlighting its potential to improve teaching and learning outcomes. It acknowledges the challenges faced by educators, such as insufficient preparation and concerns regarding academic integrity. The framework was trialed through workshops with lecturers from The Hague University of Applied Sciences, who provided positive feedback indicating a desire for clearer instructional support and increased collaboration. Findings from this trial suggest that while the SELAR framework can enhance curriculum relevance and teaching strategies, additional refinement is needed for its successful implementation in educational contexts. Overall, the document underscores the promise of generative AI in education while addressing the practical considerations that must be navigated to harness its full potential.
Key Applications
SELAR framework
Context: Higher education, specifically for lecturers at The Hague University of Applied Sciences
Implementation: Workshops were organized to gather feedback and test the application of the SELAR framework by having lecturers apply it to their learning goals.
Outcomes: Teachers effectively used the SELAR framework to integrate generative AI into their courses, which improved teaching practices and curriculum relevance.
Challenges: Educators expressed confusion about implementation details, particularly in assessing learning goals and creating AI-proof assessments.
Implementation Barriers
Knowledge and Skills Gap
There is a lack of preparedness among educators to effectively incorporate AI technologies into their curricula. Teachers require targeted professional development and support frameworks to empower them in embracing AI technologies.
Proposed Solutions: Implement targeted professional development programs and support frameworks to enhance teachers' knowledge and skills regarding AI technologies.
Assessment Integrity
Concerns about academic integrity have emerged with the advent of generative AI tools, raising questions about assessment and plagiarism. There is a need to ensure evaluations accurately reflect student learning.
Proposed Solutions: Development of AI-proof assessments to maintain academic integrity and accurately assess student learning.
Implementation Complexity
Teachers reported challenges in understanding and applying the framework for implementing AI, particularly regarding the sequence of steps and documentation.
Proposed Solutions: Provide clearer guidelines, instructional videos, and templates for documentation to assist teachers in using the framework effectively.
Project Team
Hani Alers
Researcher
Aleksandra Malinowska
Researcher
Mathis Mourey
Researcher
Jasper Waaijer
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Hani Alers, Aleksandra Malinowska, Mathis Mourey, Jasper Waaijer
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai