JKUAT Don Develops AI Course to Bridge Practical Skills Gap
The rapid rise of artificial intelligence (AI) has made tools such as ChatGPT increasingly familiar to professionals and students, but for many users, their understanding of the technology stops at generating text, answering questions or automating routine tasks. Seeking to bridge this gap, Dr. Joan Atieno Onyango, a mining geomechanics specialist and lecturer at Jomo…
Dr. Joan Onyango
The rapid rise of artificial intelligence (AI) has made tools such as ChatGPT increasingly familiar to professionals and students, but for many users, their understanding of the technology stops at generating text, answering questions or automating routine tasks.
Seeking to bridge this gap, Dr. Joan Atieno Onyango, a mining geomechanics specialist and lecturer at Jomo Kenyatta University of Agriculture and Technology (JKUAT), Department of Mining, Materials and Petroleum Engineering (MMPE), has developed a practical AI short course designed to help professionals move from simply using AI tools to understanding and developing AI solutions.
Dr. Onyango, who teaches Rock and Soil Mechanics as well as Environmental Aspects of Mining, says, her motivation arose from observing that many colleagues and professionals associate AI almost entirely with ChatGPT, despite its much broader potential in science, engineering and research.
She states that the course seeks to expose participants to the wider possibilities of AI and machine learning, while equipping them with the skills to apply the technologies to challenges within their respective fields.
“The goal is to help participants move from simply hearing about or using AI tools to understanding AI and seeing how they can apply it to offer solutions in their own fields,” she says.
Unlike courses that focus largely on introducing participants to AI tools, the training adopts a beginner-friendly, interactive and project-based approach, allowing learners to develop their skills while working on projects of their own interest.
Participants begin developing their projects from the first day, with weekly assignments designed to progressively build their knowledge and technical capabilities. By the end of the training, each participant is expected to have developed a machine-learning model that they understand, rather than simply reproducing code or following an online tutorial.
The course covers; Python programming, machine-learning workflows and approaches, model development and evaluation, generative AI and relevant computing tools.
More importantly, participants are taken through the process of identifying a real scientific or engineering problem, determining how AI or machine learning can be applied to it and progressively developing an appropriate solution.
This approach is intended to shift the focus from AI as a collection of tools to AI as a problem-solving technology.
The course also draws on examples from research and professional projects undertaken by researchers and professionals in Kenya and other African countries. This gives participants an opportunity to encounter AI through problems, data and environments that are familiar to them, while developing projects based on their own interests and contexts.
The localization of examples is particularly important in a field where much of the available AI learning material is developed around problems and datasets from other parts of the world.
Dr. Onyango adds, the course therefore seeks to demonstrate that AI can be applied to challenges across different sectors and does not have to remain the preserve of computer scientists.
Responsible use of the technology is another key component of the training. A dedicated lesson addresses accuracy, bias, privacy, data quality, transparency and ethical use of AI, with the principles reinforced as participants develop and evaluate their own projects.
This is intended to ensure learners understand not only how to build AI models, but also the importance of questioning their outputs and considering the quality and implications of the data and methods used.
For JKUAT, the initiative provides another avenue through which the university can broaden AI capacity beyond traditional computing disciplines.
Dr. Onyango believes universities have a particularly, important role in making AI expertise accessible to professionals in fields such as engineering, science, agriculture and health research, where the technology can increasingly be applied to solve sector-specific challenges.
She says, training grounded in locally relevant research and problems, can help produce professionals who are not merely consumers of technologies developed elsewhere, but who can understand, build and apply AI solutions to challenges in their own environments.