Curriculum Is Political

August 23, 2026 in news | No comments

I have been terribly busy since February this year. As a faculty member, I have taken some extra credit loads. On top of that, I served as the Chair of the Curricula and Syllabi Development Committee. This committee has developed curricula and syllabi for six programs: BSc (Eng.) in CSE, BSc in CS, MSc in CSE, BSc in AI, BSc in Data Science and Machine Learning, and BSc in Cyber Security. We already had working curricula for CSE, CS, and MSc CSE; this time, we upgraded them. We will launch the other three programs soon.

Last week, our external reviewers, Dr M Kaykobad and Dr Md Mostafa Akbar, finally reviewed and signed off on the curricula for BSc (Eng.) in CSE and MSc in CSE.

I learned a lot from the process.

 

1.

A curriculum is not just a compilation of course descriptions. It is a vision. Every vision is political; therefore, a curriculum is political.

 

2.

For a better curriculum, you need to understand the national and global economy, institutional finance, national policy, market demand, and international relations. For example, you really need to understand when a recession occurs, what stagflation is, how industrial policy works, why semiconductor dependency matters, and how Bangladesh can maintain balanced international relations with the United States, China, India, Russia, and Iran. Then suddenly, curriculum design is no longer innocent. Just three days before the election, the Yunus government signed the U.S.–Bangladesh Agreement on Reciprocal Trade; then the new government moved again with the Chinese Economic and Industrial Zone in Anwara, on government land, while pushing high-tech investment, including semiconductor chip manufacturing. Now read the situation properly: Washington is watching, Beijing is waiting, Delhi is calculating, Moscow and Tehran are not just names on a map, and YOU KNOW WHO is always standing behind the curtain. So yes, we need VLSI. We need semiconductor design. We need cybersecurity. We need AI infrastructure. A curriculum is not just an academic document; it is a survival map.

 

3.

Bureaucrats do not like updating a curriculum. But if you want to implement a curriculum, you must deal with bureaucrats. Vision alone does not move through institutions; vision needs paperwork, patience, pressure, and sometimes a very cold face.

 

4.

A curriculum can create jobs. That is why management also becomes cautious. A new program means new courses, new teachers, new labs, new budgets, new committees, new responsibilities, and new political equations inside the institution. So a curriculum does not merely organize knowledge; it redistributes power.

 

5.

You need visionary people to design a long-lasting curriculum. A curriculum cannot be designed by people who only count credit hours. Credit hours are necessary, of course, but a discipline does not emerge from credit hours alone. A discipline emerges when courses begin to speak to one another, when mathematics prepares computation, when computation prepares intelligence, when intelligence prepares industry, and when industry returns as a national question.

 

6.

But the most important lessons I learned came in two dimensions. In the technical dimension, I went through almost every course description and had the chance to understand how courses relate to one another and how a good discipline gradually emerges. In the leadership dimension, I learned that designing a curriculum requires a courageous and visionary team.

Such people are rare.

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Follow Great Problems

August 15, 2026 in Uncategorized | No comments

1.

A great question is not just a question. It travels through history and comes with a history. Newton’s law of gravitation is not just the most discussed law of nature; it also carries a genealogy from Copernicus, Kepler, and Galileo. It travels through the human mind through the crucible of human curiosity regarding the movement of heavenly bodies—particularly how our very own solar system is organized. Nonetheless, Newton’s inverse-square law of gravitation did not merely replace the old myth that the Earth stands on four tortoises; it preserved, in mathematical form, a deeper mystery: why must every object attract another object? Isn’t it like magic? Isn’t it the same old magical idea that exists within an object, only this time accompanied by an equation? So, the analogy continues, and it knocks at the door of Einstein. The questions have remained structurally related, though their forms have changed. Einstein used his very own iconoclastic approach to deconstruct Newton’s idea of gravitational magic by putting the whole problem into geometry. Thus, the question of how celestial bodies move has come all the way to the twentieth century, and now we have our modern understanding.

 

2.

Great questions also act like destinations; they expose the limits of human understanding within a tradition of thinking, and therefore great problems are usually hard problems, not because they are merely difficult to solve, but because, in trying to solve, break, or transform them, human thought is often forced to invent a new realm for itself, as happened when the non-Euclidean geometers entered into their long intellectual war with Euclid’s fifth postulate and eventually opened the door to non-Euclidean geometry.

 

3.

Anyone who is in the pursuit of research in any field must understand the genealogy and the hardness of these problems. It is impossible to predict whether problems of such difficulty can ever be solved, but these problems can be used as lighthouses for research, allowing a thinker to travel in their direction. If they attempt to solve those exact problems, they might fail; yet it is safe to try to solve similar problems by isolating a simplified echo of the grand problem under gentler constraints, provided that this thinker explores the problem from different perspectives. They must be able to define their own terms in order to understand these problems—to truly understand the core structure of these limiting, monumental challenges.

 

4.

I think this century will be different, particularly in the arena of intellectual pursuit, as AI is going to influence human intelligence by changing how people study, understand, and even mistake fluency for mastery. A genuine researcher must therefore treat great, monumental problems as guiding stars, because the machine often offers only a superficial illusion of mastery, masking the fact that it has sidestepped the core crisis. A computer does not escape formal limits merely because its outputs appear fluid or intelligent. Whether we describe those limits through computability, formal logic, complexity, representation, or learning theory, AI remains bound to the deeper constraints of computation and mathematics. Therefore, a computer science researcher must remain deeply aware of the most difficult and limiting problems in computation and mathematics.

 

5.

If we try to pose a recipe for the researcher, it might look like the following:

algorithm researcher_recipe:
    step 1: understand the core structure of a limiting problem.

    step 2: observe how the problem hides inside ordinary mathematical or computational practice.

    step 3: define the problem in your own terms.

    step 4: ask whether the question can be eliminated, broken, or transformed without damaging the discipline.

    step 5: if the problem can be reduced:
                identify the fundamental assumptions of the discipline.
                ask which assumptions can be attacked.
                ask whether new assumptions make the problem solvable.
                ask whether any old assumptions can then be removed.
                ask whether these assumptions are true, or merely functional within a particular plane of thought.

    step 6: ask whether the dimension of the thought-plane itself can be extended.

    // TO BE CONTINUED...

Use code with caution, sweetheart!

 

6.

There is no return 0 unless she starts walking toward great problems.

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