Our Mission
Our mission is to provide insights and opportunities to educators and researchers interested in this emerging field, and to promote collaboration within the AAEE community.
Our Story
AAIEEC was founded by a group of AAEE members who recognised the potential of genAI to transform engineering education. Our community includes experts in the fields of AI, engineering education, and curriculum development. Together, we are working to develop innovative ways to integrate genAI into engineering education, and to share our knowledge and expertise with the wider community.
Committee Meeting Minutes
Minutes from committee meetings will be published here after the minutes have been approved
2024 Meeting 1 - 05 Feb - Approved 06 June 2024
2024 Meeting 2 - 06 Jun - Approve 20 September 2024
2024 Meeting 3 - 20 Sep - Minutes not yet approved
Aims
We aim to engage in the following ways:
01
Curriculum Development:
Designing AI-focused modules or courses within engineering programs to ensure students are well-versed in AI principles relevant to their field.
02
Research Collaboration:
Facilitating collaborative research efforts among educators to explore innovative AI applications in engineering education.
03
Knowledge Exchange Hub:
Creating a platform for sharing best practices, teaching methodologies, and resources for integrating AI into engineering curricula
04
Professional Development (Empowering Educators):
Organizing workshops, seminars, and training sessions to enhance educators' AI knowledge and teaching skills.
05
Student-centric Exploration:
Engaging students in captivating AI-related projects and learning experiences, sparking curiosity and practical application.
07
Diversity and Inclusion:
Fostering diversity and inclusion within AI in engineering education, ensuring equitable access to resources and opportunities.
06
Ethical Implications:
Addressing ethical considerations and societal impacts of AI within engineering education, promoting responsible AI development and use.
08
Evaluation and Assessment:
Developing assessment frameworks to measure the effectiveness of AI integration in engineering education and refining teaching methodologies based on outcomes