Artificial intelligence is gradually transforming the way institutions of higher education create their class schedules.
Analyzing thousands of constraints, detecting conflicts, generating scenarios, suggesting alternatives… Tasks that used to take a lot of time can now be completed much more quickly thanks to algorithms and AI.
So, naturally, a question arises: if AI can plan, will we still need planners and educational leaders in the future?
And that is precisely where the line is drawn between what AI can automate and what requires human expertise.
Because planning isn't just about doing calculations.
What types of AI are we talking about when it comes to academic planning?
Since the advent of ChatGPT, Claude, and other large language models (LLMs), artificial intelligence has often been equated with generative AI. However, when it comes to AI applied to academic planning, different technologies come into play. They do not serve the same purpose or have the same applications.
Scheduling Engines: Generating Schedules
Scheduling engines automatically assign courses to available time slots and resources. They take into account the constraints specified by the institution: faculty availability, classroom capacity and equipment, student groups, permitted time slots, academic rules, campus constraints, and so on. Their role is to create a feasible schedule based on a complex set of constraints.
Optimization Engines: Improving Planning
Just because a schedule has no conflicts doesn’t mean it’s necessarily optimal. Optimization engines go a step further by comparing numerous combinations to improve specific criteria: minimizing wait times for students, better distributing courses, optimizing room occupancy, or best meeting the preferences defined by the institution . Their role is to find the best compromises based on the established objectives.
Predictive AI: Anticipating Needs and Simulating Scenarios
Based on historical data and available information on the institution’s operations, predictive models can help identify trends and anticipate certain needs—such as changes in enrollment, resource requirements, space utilization, or capacity. Planning is then no longer just about organizing the coming week or semester; it also becomes a tool for anticipation and management support.
Conversational AI: Supporting Teams in Their Day-to-Day Work
Finally, large language models can assist teams with additional tasks: summarizing a request, searching for or rephrasing information, preparing a communication following a schedule change, or facilitating certain administrative tasks.
This distinction between the different technologies is important.
To understand how usage patterns are changing, check out our article on academic planning in the age of AI.
CWhat AI Does Better Than We Do
Let’s consider a scheduling problem involving multiple programs, hundreds of courses, dozens of classrooms, full-time and part-time faculty, multiple campuses, and numerous scheduling and academic constraints.
How many possible combinations are there? Far too many to test them one by one.
That is precisely where computing power really comes into its own.
A planning engine can explore a very large number of possibilities, detect incompatibilities, compare different scenarios, or search for new solutions when a constraint changes.
Is a teacher unavailable? Is a classroom no longer available for a class? Does a class need to be rescheduled?
The tool can search for alternatives that are compatible with the constraints already entered and enable planning teams to identify possible options more quickly.
The time savings can be significant.
Did you know? With Adesoft, cut the time spent creating schedules in half and reduce the time needed to manage unforeseen events by up to 80%.
What AI Can't Replace: Human Expertise
An algorithm operates based on the data, constraints, and objectives it is given. However, in a higher education institution, not all rules are formalized in the tools.
Why is it better to reschedule one class rather than another? Why set aside a time slot for a guest speaker? Which restriction should be relaxed when no scenario satisfies everyone?
These decisions require an understanding of the programs, the teams, and the realities on the ground. They also involve assessing the impact of a change on students, faculty, enrollment, and available resources.
An optimal solution for an algorithm is therefore not necessarily the most pedagogically appropriate solution.
AI can identify possible scenarios. It’s up to the teams to put them into context, prioritize them, and weigh the options to choose the one that best meets the institution’s needs. Because the question isn’t just, “Which combination works?” It’s also, “Which trade-off makes the most sense?”
How AI Is Transforming the Field of Instructional Planning
AI does not, therefore, eliminate planning jobs. However, it can change the nature of those roles.
Today, a significant portion of teams’ time is still spent on a series of tasks: collecting → entering → placing → verifying → moving → correcting.
With optimization and decision-support tools, this process can gradually evolve to: define → simulate → compare → decide → manage.
The value of teams, then, no longer depends solely on their ability to create a complex schedule, but rather on their ability to define the right constraints, analyze the proposed scenarios, and choose the appropriate trade-offs.
This also requires adopting new habits: ensuring data quality, understanding the limitations of the tools, and maintaining a critical perspective on the recommendations generated.
Automating part of the planning process does not mean automating accountability.
AI does the calculations. Humans make the decisions.
AI and algorithms can already speed up certain stages of academic planning: analyzing constraints, detecting conflicts, exploring scenarios, or searching for alternatives.
But behind a schedule, there's never just a mathematical problem.
There are students, teachers, educational goals, limited resources, and compromises to be made.
AI does not, therefore, eliminate the expertise of planning teams; rather, it shifts the focus of that expertise from manual construction to analysis, decision-making, and oversight.
AI calculates the possibilities. It’s up to the teams to determine which ones actually make sense.