DIPPER Lab Leads Engineering AI Dialogue at IET-Ghana CPD Session
Dr. Andrew Selasi Agbemenu, Deputy Scientific Director at the Distributed IoT Platforms, Privacy, and Edge-Intelligence Research (DIPPER Lab), was the featured speaker at a Continuing Professional Development (CPD) session organized by the Institution of Engineering and Technology, Ghana (IET-GH), under the theme “AI Tools for Engineers.”
The session focused on practical approaches to integrating Artificial Intelligence, particularly Large Language Models (LLMs), into engineering workflows, a growing area of relevance for both practitioners and researchers.
Drawing from ongoing research at DIPPER Lab, Dr. Agbemenu introduced participants to the CLEAR Prompting Framework, a structured method for engaging productively with generative AI tools in technical contexts.
Originally developed by Anthropic, the CLEAR Framework is used extensively in DIPPER Lab’s work to improve prompt engineering, model alignment, and reliable AI integration within engineering workflows.
This framework, rooted in DIPPER’s broader research on distributed AI systems and edge intelligence, was designed to help engineers generate precise and context-aware outputs from AI models.
He also provided insights into, How LLMs reason through engineering problems, The role of AI agents in automating engineering tasks, Best practices for safely integrating AI into engineering decision-making, Human-AI collaboration across design, testing, and operational stages, Ethics, limitations, and security concerns in AI-enhanced environments.
"LLMs are powerful assistants. They are here to stay, not as threats, but as tools. The responsibility now lies in how we use them,” Dr. Agbemenu noted.
“We must secure our data, become fluent in AI interaction, and continuously grow our technical literacy,” he added.
Dr. Selasi Agbemenu, further reinforced the importance of AI literacy for engineers, emphasizing the need to build fluency, evaluate quality of outputs, and uphold professional judgment when integrating AI into core technical functions.
These discussions align with DIPPER Lab’s mission to bridge emerging technologies with applied engineering practices.
The lab continues to lead in developing scalable frameworks that ensure ethical, efficient, and secure deployment of AI in engineering environments.
