Summary: Each unit of say 7 lectures must include a lecture on practical implementations with command-line examples. These can be evaluated in lab/tutorial classes where students replicate it. Students must be familiarised with latest datasets. Students must test on popular datasets. Course outcomes should be updated to reflect this motto. One unit in each course must be reserved for industry exposure in the subject. It’s time to end practical classes with implementations of code as an assignment for basic topics like queues, search algorithms and stacks. It’s time to expose students to Python implementations and the visualizations that follow. Coding is being automated by AI. Stop assigning lengthy coding tasks to students; instead, tell them to use Python to gain exposure to libraries and implement topics in most courses. To expedite it startups can tie up with universities in providing the practical implementation part. Industry can provide their labs in universities to teach as well as showcase their products and how they complement applications in society and world on large.
In changing times, we want our younger students to be ready for the job market. In these times, we need a constantly updated course curriculum. We need to ensure the latest from the industry is taught as the final unit in a five-month semester syllabus. If the course has 46–48 lectures, at least 6–8 must be dedicated to the latest developments in the subject from an industry perspective. The faculty must get training on the same before the commencement of the course. Or we can let some startup company help the university with this course objective.
For example, in a course: “Introduction to data structures,” if the syllabus is divided into 7 units. Then the last unit must be the latest in the industry in the subject. The faculty must do research work in finding what the latest is and should update the syllabus before the session starts, with help of start up company, indeed before registration of the subject is done. This can be updated teaching of latest libraries, newer versions of methods, to even visualizations with the help of AI.
This shall update the students with the latest in that subject from an industry perspective. Further, each of the rest of the unit, one lecture must be assigned to practical applications of theory.
This is a huge change, but we need it as times are changing, and we don’t want our students to lag behind in the international job market.
Let me explain this to you in more detail with my specialization in “Soft Computing”. The course can be divided into 5 units:
Unit 1. Fuzzy Logic, Rough Sets, and its applications
Then one lecture on how to use software for Fuzzy logic on Python, R, Matlab, and more. Teaching students the command line to run these libraries, along with visualizations in the outputs and dataset examples provided directly from GitHub. Teaching students real life examples of these systems in robotics say. This can be expedited with help of sharp focused start up companies tie ups.
Unit 2. Supervised learning, reinforcement learning, and unsupervised learning. How the Human Brain Learns.
Then one lecture on how to run Learning Algorithms, Clustering, and Reinforcement learning in Python. Using the latest libraries. Teaching students the command line to run these latest libraries, along with visualizations in the outputs and dataset examples provided directly from GitHub or other standardized sources, including Hugging Face.
Unit 3. Neural Networks and Fuzzy Systems
Then one lecture on how to run these on global databases. Using the latest libraries. Teaching students the command line to run these latest libraries, along with visualizations in the outputs and dataset examples provided directly from GitHub or other standardized sources, including libSVM.
Unit 4. Genetic Algorithms and Evolutionary Algorithms
Here, all algorithms would be supported with a real-world UI to show students how soft computing is used in real time. The output would be shown as a confusion matrix, and other parameters must be taught. These can include running medical datasets and applications varying from classification to robotics. Datasets must be different from those taught above.
Unit 5. Latest from Industry.
This can vary from applications in machine translation to traffic management to helping people with disabilities. These topics are sensitive and need soft computing tools. All this with practical applications hence we now need help of industry in teaching courses from computer science and other departments.
All tutorial classes would now involve reproducing what the faculty presented in real-world applications of the core objectives of the subject.
Special guest lectures from start-up companies should be part of lectures on and off.
This is just one example.
Another example is my other specialization: Data structures. Here, graphical interactive content must be presented to explain concepts such as queues, stacks, linked lists, trees, searching techniques, and graphs.
Here, students must be able to develop these visualizations and implement the libraries in Python, rather than relying on the age-old practice of implementing stacks and queues from scratch in C++. Time to implement data structures is gone. In today’s world, we must use libraries that provide these data structures and their search algorithms.
We must tell students what libraries are, how to use Python libraries, and how to generate visualizations from them. Today, we all know that code is being generated by AI, so why waste students’ time implementing a stack from scratch? Why not teach students to use advanced libraries?
This is what we need to do in lab classes. All this with updated course objectives and course outcomes. So here, we need a firm change in the course objectives and outcomes. Course outcomes must include exposure and proficiency in the latest state-of-the-art libraries in the topics studied in theory classes. These must include command lines to run the libraries, the parameters, and instructions for presenting the output as visualizations. All this can be done on time with a catch up from start up companies with bright minds.
If these things are added to the university course curriculum, students won’t feel doubtful while applying for jobs in the competitive world we are living in neither would students feel being left out.
Thank you for reading.
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Artificial Intelligence and Future 