Artificial intelligence is changing more than the way students write code. It is also forcing universities to reconsider how programming knowledge should be tested.
Tools such as ChatGPT and GitHub Copilot, both included among the top AI tools in 2026, can now generate functional programs, explain algorithms, identify errors and suggest alternative solutions.
In response, computer science departments are moving away from assessments based only on writing code from scratch. Greater attention is now being given to debugging, code comprehension, oral examinations, software projects and the ability to explain technical decisions.
An international survey conducted by the ACM Task Force on Generative AI and Programming Assessment provides evidence that this change is already happening. The survey collected responses from 763 computer science educators across 49 countries, offering a broad picture of how universities are adapting to generative AI.
The findings suggest that AI-era testing is not simply a temporary reaction to ChatGPT. It represents a wider transformation in what universities believe future software developers need to learn.
Global Survey Shows Computer Science Assessment Is Changing
The ACM Task Force survey indicates that educators around the world are reconsidering traditional programming assessment methods.
For decades, computer science students were commonly evaluated through laboratory exercises, take-home assignments and practical examinations. In most cases, students were expected to build a program independently and submit the final code for grading.
That model worked when writing the code was the main evidence of programming ability. Generative AI has weakened that assumption.
A student can now enter an assignment into an AI tool and receive a complete solution, often accompanied by comments, explanations and debugging suggestions. Even when the generated code is imperfect, the student may need only minor changes before submitting it.
As a result, instructors can no longer rely on the final output alone.
They must also determine whether students understand the code, can identify its limitations and are capable of modifying it when the requirements change.
The survey shows that many educators are already redesigning assessments around these abilities. Rather than asking only whether a student can produce working software, instructors increasingly want to know whether the student can reason about software.
Why Traditional Programming Assignments Are No Longer Enough
Traditional coding assignments usually measure whether a student can translate a clearly defined problem into a working program.
Generative AI can now perform much of this process. It can produce common algorithms, generate website components, write database queries, create test cases and fix basic syntax errors.
This does not make programming knowledge irrelevant. Instead, it changes the type of knowledge that assessments need to measure.
A completed program may look correct while containing security weaknesses, inefficient logic or incorrect assumptions. Students who rely too heavily on AI may submit working code without understanding why it works or when it could fail.
Modern assessments therefore need to answer deeper questions:
- Can the student explain the logic of the program?
- Can they find hidden bugs or security risks?
- Can they compare different technical approaches?
- Can they improve inefficient AI-generated code?
- Can they defend their decisions during questioning?
These tasks are more difficult to complete through AI assistance alone because they require personal understanding, technical judgment and the ability to respond to new information.
Debugging and Code Comprehension Are Becoming Core Skills
One of the most notable findings from the survey is that 64% of educators now prioritise debugging and code comprehension over writing code entirely from scratch.
This change reflects the realities of professional software development.
Developers do not spend every working day creating new applications from an empty file. Much of their time is spent reading existing code, investigating errors, reviewing contributions, improving performance and maintaining systems created by other people.
AI-generated software creates the same need. Although an AI assistant may produce code quickly, a human developer still needs to verify its accuracy, security and suitability.
Universities are therefore introducing assessments in which students may be asked to examine unfamiliar code rather than create a complete solution independently.
For example, students might need to identify a logical error, explain why a program produces unexpected results or improve an inefficient algorithm. They may also be asked to review an AI-generated solution and describe its strengths, weaknesses and potential risks.
These exercises reveal whether students understand programming concepts beneath the surface.
A learner who can generate code but cannot explain or repair it may struggle in a real software environment. By contrast, a student who can analyse an unfamiliar system, locate problems and justify improvements is demonstrating a more transferable form of programming competence.
Oral Exams Are Returning to Programming Courses
The growing use of generative AI is also contributing to the return of oral examinations.
Oral assessments have not traditionally been a major part of every computer science programme. However, they provide educators with a direct way to verify whether students understand the work they have submitted.
During an oral examination, an instructor may ask a student to walk through an algorithm, explain a design decision or describe how the software would behave under different conditions.
The educator can then ask follow-up questions based on the student’s answers.
This real-time interaction makes it harder to depend on memorised or AI-generated responses. Students must demonstrate that they can think through the problem and communicate their reasoning clearly.
Oral exams may also reveal misunderstandings that are not visible in a finished assignment. A student may have working code but be unable to explain an important function, data structure or security decision.
Although oral examinations require more staff time, they can provide a more reliable picture of individual understanding. Some universities may therefore use short technical interviews or project defences alongside written and practical assessments.
Project-Based Learning Is Replacing Isolated Coding Tasks
Another major change is the growing use of project-based assessment.
Short programming exercises usually test one narrow skill, such as implementing a sorting algorithm or creating a specific function. AI tools can often complete these tasks with minimal guidance.
Longer software projects are more difficult to reduce to a single prompt.
A project requires students to interpret requirements, plan the system, manage changes, test features, document decisions and respond to problems over time. Students may also need to collaborate through version-control platforms and divide responsibilities among team members.
This allows educators to assess a wider range of abilities, including software design, teamwork, communication, testing, debugging and project management.
AI tools may still be permitted during the project. However, students can be required to document how they used AI, verify generated outputs and explain which decisions remained their responsibility.
Regular progress reviews can also help instructors confirm ownership of the work. Instead of grading only the final product, educators can evaluate planning documents, code revisions, testing records, presentations and individual reflections.
This approach is closer to professional software development, where successful outcomes depend on more than producing code quickly.
Universities Are Redefining Responsible AI Use
The rise of AI-era testing does not necessarily mean universities will ban ChatGPT or GitHub Copilot.
The growth of AI tools designed for educators also shows why institutions need transparent rules governing classroom use, assessment and student accountability.
The challenge is to distinguish responsible assistance from inappropriate dependence.
Students may be allowed to use AI to explain a difficult concept, brainstorm solutions, improve code readability or identify possible errors. However, they may still be expected to verify the response, acknowledge the assistance and demonstrate personal understanding.
Responsibility remains with the student.
AI-generated code can contain inaccurate assumptions, outdated methods, security vulnerabilities and fabricated information. Accepting its output without review can produce serious consequences, particularly in systems involving personal data, financial transactions or public safety.
Teaching responsible AI use therefore involves more than showing students how to write effective prompts. It requires critical evaluation, secure coding, ethical judgment and accountability.
Universities are beginning to treat these abilities as essential components of computer science education.
AI Is Changing Computer Science Learning Objectives
Programming courses have traditionally focused heavily on syntax, language rules, algorithms and the ability to build software independently. The expanding use of AI-powered study tools means students must also learn how to question, verify and responsibly use machine-generated learning material
Those foundations remain important, but they are no longer sufficient.
Students entering an AI-assisted software industry will need to know how to collaborate with intelligent tools without surrendering human judgment. They must be able to recognise when an AI response is incorrect, incomplete or unsafe.
New learning objectives are therefore likely to include:
- Reviewing and validating AI-generated code
- Detecting logical errors and security vulnerabilities
- Breaking complex problems into manageable tasks
- Comparing alternative software designs
- Explaining technical decisions clearly
- Understanding ethical and academic responsibilities
- Combining human expertise with AI-assisted development
These capabilities place greater emphasis on reasoning and communication.
The strongest programmer may no longer be the person who types code fastest. It may be the person who understands the problem most clearly, asks the right questions and can determine whether a proposed solution is reliable.
Challenges Educators Still Need to Address
Although AI-era testing offers opportunities to improve programming education, implementing it will not be simple.
One challenge is workload. Oral exams, personalised questioning and detailed project reviews require more instructor involvement than automatically grading a coding exercise.
Large classes may find it difficult to provide every student with an individual technical interview or extensive project feedback.
Consistency is another concern. Universities need clear marking criteria so that oral and project-based assessments remain fair across different students, instructors and campuses.
Access to AI tools may also create inequality. Some students can afford premium AI subscriptions or more powerful devices, while others may have access only to limited free versions. Institutions will need policies that prevent technology access from determining academic performance.
Educators also require training. Instructors must understand what AI tools can do, where they fail and how students may use them during assignments. Assessment methods will need regular review because generative AI is developing rapidly.
A task that effectively tests independent reasoning today may become easy for AI systems in the future.
What AI-Era Assessment Means for Employers
Changes in university assessment may eventually influence the way technology companies recruit junior developers.
Many technical interviews still focus on solving algorithms under strict time limits. While these exercises can reveal problem-solving ability, they do not always reflect the daily responsibilities of a software engineer.
Modern developers frequently work with existing codebases, review pull requests, investigate production issues and collaborate with AI-powered tools.
Graduates educated through AI-era assessment may therefore be particularly strong in code comprehension, debugging, communication and technical evaluation.
Employers may begin placing greater value on candidates who can examine an unfamiliar program, identify weaknesses and explain how they would improve it.
Technical interviews could include code-review exercises, pair-programming sessions, debugging scenarios or discussions about software architecture. Candidates may also be asked to assess AI-generated code rather than pretend that AI tools do not exist.
The purpose would not be to test whether a candidate can compete with AI in code generation. It would be to determine whether the candidate can use AI safely and make better engineering decisions.
The Future of Programming Assessment
Future computer science assessments are likely to combine several methods rather than rely on one type of examination.
Students may complete software projects, defend their decisions orally, review unfamiliar code and participate in live debugging sessions. Reflective journals and development records may be used to show how a project evolved.
Some assessments may allow AI openly. In these cases, students could be graded on the quality of their prompts, their verification process and their ability to improve the generated output.
Other assessments may remain AI-free to confirm that students understand essential programming foundations.
The most effective model may be a balanced one.
Students still need enough independent knowledge to recognise when an AI system is wrong. At the same time, they need experience using the tools they are likely to encounter in professional software development.
AI-era testing is therefore not about lowering academic standards. It is about updating those standards so that they measure meaningful abilities.
Conclusion
Generative AI has made it possible to create functional code faster than ever, but producing code is only one part of software development.
The international ACM Task Force survey shows that computer science educators are responding by placing greater emphasis on debugging, code comprehension, oral assessment and project-based learning.
This transformation could strengthen both academic integrity and career preparation. Students will still need programming foundations, but they will also need to understand, verify and improve the work produced by intelligent tools.
The future of computer science education is therefore not a competition between students and AI. It is a shift towards teaching students how to use AI while preserving human reasoning, responsibility and technical judgment.
