AI coding tutors that debug with you, not for you
Paste a broken function into any modern AI tool and you’ll have a corrected version in seconds. That’s genuinely useful when you need to ship something. But if you’re trying to learn to program, that instant fix might be working against you.
Debugging is a skill in itself. The process of isolating a fault, identifying the wrong assumption, making a targeted change, and verifying it actually resolved the issue is where a lot of real understanding gets built. When an AI skips straight to the answer, that process disappears.
The author of this piece explored that tension hands-on with Coddy.tech, an interactive coding-learning platform that pairs exercises and test feedback with an AI tutor called Bugsy. The core question: how much help should an AI coding tutor give before it simply gives away the answer?
The Problem with Paste-and-Fix
Consider this Python function:
def calculate_average(numbers):
total = 0
for number in numbers:
total += number
return total / (len(numbers) - 1)
scores = [80, 90, 70, 100]
print(calculate_average(scores))No syntax errors. No exceptions. But the result is wrong. Four scores total 340, so the correct average is 340 / 4 = 85. The function divides by 3 instead, because of the - 1 in the return statement.
An AI assistant could immediately return return total / len(numbers) and be done. Problem solved, reasoning skipped. A better response would be: “Your logic calculates the total correctly. But take a closer look at the divisor. How many values are actually in numbers?” That small difference represents two very different approaches to assistance.
️ A Five-Stage Debugging Framework
The article proposes a five-stage model for how AI assistance should work in a learning context:
Context → Diagnosis → Hint → Verification → Explanation
1. Context
Before suggesting anything, the assistant needs to understand the goal. That means the problem statement, the current code, expected output, actual output, any errors, and previous attempts. Without this, technically valid advice can still be wrong for the actual requirement.
Take def is_adult(age): return age > 18. Is that correct? It depends entirely on whether the spec says “older than 18” or “18 or older.” The code alone can’t answer that question.
2. Diagnosis
Once context is clear, the assistant identifies what’s likely wrong, without necessarily saying what code should replace it. For the average example: “The total is calculated correctly, but the divisor doesn’t match the number of values in the list.” That narrows the problem without solving it.
3. Hint
If diagnosis isn’t enough, a more specific nudge: “Check what len(numbers) returns for the sample input and compare it with the divisor in your return statement.” The learner still has to make the correction. The article describes this as a hint ladder: Observation → Direction → Stronger Hint → Explanation → Solution.
4. Verification
Fixing one failing test isn’t enough. After correcting the average function, you’d want to test edge cases:
calculate_average([80, 90, 70, 100]) == 85calculate_average([10, 20]) == 15calculate_average([5]) == 5calculate_average([])— division by zero
The original bug is gone, but the empty-list case exposes a new one. A good AI tutor should encourage thinking about what else could fail, not just confirm the fix worked on one example.
5. Explanation
After the fix, reinforce the concept: “An average divides the sum by the count of elements. Because the list has four values, subtracting one caused division by three instead of four.” Explanation after the fact reinforces reasoning rather than replacing it.
What Coddy Actually Does
The author tested this against Coddy.tech exercises at two difficulty levels.
Beginner: Python line comments
The task was to comment out print("Goodbye!") without deleting it. Simple requirement, but the workspace was the interesting part: challenge requirements, a browser-based Python editor, Run Code, test results, expected output, multiple hint levels, solution access, an explain option, and Bugsy all in one place.
When an incorrect solution was submitted, Coddy’s test feedback connected the failure directly to the requirement. Expected output was displayed alongside actual output. Progressive hints were available: the first directed toward adding # at the beginning of the appropriate line, with additional hints available if needed. The full solution remained a separate, deliberately locked action. The path from failure to answer looked like: Attempt → Test feedback → Hint 1 → Hint 2 → Hint 3 → Solution.
Bugsy also caught a secondary problem in the submitted code: an incorrectly formed closing quote on the Hello statement. That error wasn’t part of the concept being taught, but it was present in the actual code, which suggests Bugsy was responding to both the exercise context and the current editor state.
Medium: Library catalog search
The harder challenge required implementing find_book_descriptions(catalog, query): iterate a two-dimensional catalog, do case-insensitive matching on both ID and description, collect matching descriptions, join results with newline characters, and return “No books found.” when nothing matched.
The author submitted a broken implementation mixing Python with pseudocode. Multiple test cases failed. Three separate feedback mechanisms were available:
| Mechanism | Question it helps answer |
|---|---|
| Test Cases | Does my implementation behave as expected? |
| Debug | Where is execution currently failing? |
| Bugsy | What may be wrong with my approach, and how can I move forward? |
The Debug panel surfaced the immediate Python failure: SyntaxError: invalid syntax (main.py, line 5). Bugsy went further. It recognized the pseudocode mix, pointed toward creating a list for matching descriptions, iterating through books, using lowercase comparisons, and appending the description rather than the query.
It also caught a control-flow issue: the “No books found” decision was being evaluated inside the search loop. If the first book doesn’t match but the second does, the function could return the wrong result before finishing the search. That’s not syntax correction. It requires understanding the relationship between the requirement and loop logic.
The Hard Design Question
The medium challenge also exposed the core tension. Bugsy didn’t stop at identifying problem areas. It provided enough structural detail that a learner could piece together most of the implementation from the response.
For an experienced developer trying to move fast, that’s fine. But for someone trying to learn control flow, there’s a real question about whether more guidance is always better. The article frames it clearly:
An AI assistant capable of generating the complete solution still has to decide whether generating it is actually the most useful thing to do.
The appropriate amount of help also depends on who’s asking. A beginner learning loops for the first time may benefit from progressive hints. An experienced developer debugging an unfamiliar library may want the answer immediately. The article suggests that an ideal interaction might start by asking: do you want a hint, an explanation, or the corrected implementation?
A Better Evaluation Rubric for AI Coding Tutors
Most benchmarks for coding assistants measure whether they produce correct code. For learning-focused tools, the article argues for additional test cases:
- Syntax error: Does it correctly locate the problem?
- Runtime error: Does it explain why execution failed?
- Logic error: Can it diagnose without rewriting everything?
- Boundary condition: Does it understand zero, empty input, or equality thresholds?
- Wrong algorithm: Can it guide toward the right concept?
- Repeated wrong attempts: Does assistance adapt across attempts?
- Correct implementation: Does it recognize nothing needs fixing?
- Alternative valid solution: Does it accept a different-but-correct approach?
That last two deserve emphasis. An AI that suggests changes to already-correct code is producing false positives. And two implementations of is_even() can both be correct even if one uses number % 2 == 0 as a return expression and the other uses an if block. Conflating “different from the reference solution” with “incorrect” is a real failure mode.
On repeated failures, the better path is escalating specificity, not repeating the same hint or immediately dumping the full solution. Something like: first attempt gets “look closely at the operation you’re using,” second attempt gets “division gives a quotient, think about what returns the remainder,” third attempt gets “in Python, % returns the remainder after division.”
The Verdict
Coddy’s most interesting design choice isn’t Bugsy. It’s the combination: structured learning path, executable exercises, test feedback tied to requirements, a debug tool, layered hints, and AI assistance, all in one workspace. Each component serves a different function, and together they create a workflow closer to Learn → Code → Run → Fail → Inspect → Debug → Ask for Help → Retry rather than Problem → Ask AI → Copy Answer.
The harder question, which the article leaves genuinely open, is how much the AI should reveal and when. That design decision matters as much as the model’s capability. The most useful AI coding assistant isn’t always the one that generates the most code. Sometimes it’s the one that knows when to stop.
I’m founder of BizStack at Cagri Sarigoz LLC and a passionate advocate for entrepreneurs.
With over 14 years in tech, marketing, and AI, including my role as Head of SEO at CitizenShipper and co-founder of TaleBot at Intale AI, I’m dedicated to sharing genuine, useful product insights and tips.
At BizStack, I aim to cut through the digital noise to provide clear, actionable advice.
And more than all else, I’m a father to a (always) little girl and a husband.
Contact me at [email protected] for assistance.
