Learning | Dr. Mauricio Zambrano-Bigiarini

How to use ChatGPT to learn Hydrology - Part I: configuration, context and useful prompts

Sun, 16 Aug 2026 00:00:00 +0000

Estimated reading and practice time: 15-25 minutes.

Why this series of tutorials?

Preparing university students today for their future professional practice is unusually challenging because we cannot know precisely what Artificial Intelligence systems will be able to do in a few years. The capabilities of generative models are evolving rapidly, barriers to access are falling, and the range of tasks that can be automated is changing. Teaching only how to operate the current version of ChatGPT therefore has limited long-term value: the tools will change, but engineers will still need to understand a problem, judge the quality of an answer, verify evidence, detect errors and make responsible decisions.

Recent discussion in higher education points in the same direction. AI can broaden access to explanations, feedback and personalised learning, but it can also foster dependence, superficial learning and reduced exercise of critical thinking when it replaces the student’s own cognitive effort ( ). The challenge is therefore not simply to adopt AI more quickly, but to decide what it should be used for and what kind of learning we want it to support ( ). Likewise, recent analyses argue that higher education should strengthen capacities such as critical thinking, adaptability, curiosity and lifelong learning ( ), and that many of the decisions posed by AI are fundamentally pedagogical before they are technological ( ).

For that reason, the purpose of this three-part series is not to teach you how to obtain answers more quickly. It is to help you use generative AI to understand Hydrology more deeply, practise more effectively, identify what you still do not understand, and develop the autonomy required to solve unfamiliar problems. In short:

Use AI to increase learning, not to avoid the process of learning.

ChatGPT can help you learn Hydrology, or it can help you avoid learning it. The difference depends mainly on how you use it.

In IIO422-Hydrology, obtaining the correct numerical answer is not enough. The course expects you to analyse hydrological processes, quantify variables, work probabilistically, interpret results, detect errors and justify engineering decisions. Generative AI is explicitly permitted as a learning and computational support tool outside assessments, but its use must be critical, verifiable, transparent and responsible.

1. The fundamental rule: tutor, not autopilot

A weak query would be:

Explain the water balance.

A much better one would be:

I am taking IIO422-Hydrology in Civil Engineering.

I understand precipitation and streamflow, but I still confuse
evapotranspiration with changes in catchment storage.

Help me interpret the annual water balance physically.
First ask me two diagnostic questions.
Then correct my misconceptions and only afterwards give
a concise explanation.

Use units of mm/year and do not assume Delta S = 0
unless you justify that approximation.

The difference is not cosmetic. The second prompt forces you to think before receiving the explanation.

2. Configure the context that actually matters

The course assumes previous knowledge of Fluid Mechanics, Hydraulics, Statistics and English. It also requires you to collect and analyse hydrometeorological information, construct simplified water balances, perform frequency analysis, and estimate design rainfall and streamflow.

In Settings > Personalization > Custom Instructions, you could write:

I am a Civil Engineering student in Chile taking IIO422-Hydrology.

I have already passed Fluid Mechanics, Hydraulics and Statistics.
I also know how to program in R.

When helping me:
- prioritise reasoning and physical interpretation;
- use SI units;
- check dimensional consistency;
- distinguish data, assumptions, method, result and uncertainty;
- use examples from Chilean catchments when useful;
- do not invent data, references, packages or functions;
- if important information is missing, ask before assuming;
- for R, avoid tidyverse and prioritise base R, hydroTSM
 and the tools specified in the course;
- when I am preparing for an assessment, do not provide
 the full solution before I have attempted the problem.

Do not include personal information that is irrelevant to learning.

3. Connect ChatGPT with the Learning Outcomes

The syllabus defines five Learning Outcomes. Use them as a test of whether an AI interaction is actually helping you learn:

LO1: conceptually analyse key hydrological-cycle processes and apply them to Chilean catchments.
LO2: quantify the temporal evolution of precipitation, evapotranspiration and runoff using in-situ and gridded data.
LO3: analyse hydrological variables probabilistically for engineering design and planning.
LO4: incorporate social responsibility into water-resources management problems.
LO5: develop personal and teamwork skills needed to meet agreed objectives and deadlines.

A useful ChatGPT session should strengthen at least one of these outcomes. If it merely produces text or code that you cannot explain, it probably strengthens none of them.

4. The anatomy of a good prompt

For most course tasks, five components are sufficient:

ROLE + CONTEXT + TASK + CONSTRAINTS + OUTPUT

Role

Act as a university Hydrology lecturer for Civil Engineering.

Context

I am studying infiltration and the Green-Ampt method.
I understand infiltration capacity, but not the role
of initial soil moisture.

Task

Help me compare the same storm over an initially dry soil
and an initially wet soil.

Constraints

Do not invent parameter values.
Use SI units.
Do not simplify the assumptions of Green-Ampt without stating them.

Output

Answer in five parts:
1. physical idea;
2. relevant variables;
3. equation;
4. example;
5. one question to test my understanding.

5. Make the model recognise missing information

A hydrological answer may depend on:

temporal scale;
catchment area;
data resolution;
hydrological year;
antecedent conditions;
rainfall- or snow-dominated regime;
regulation;
units;
availability and quality of observations.

Add:

Before solving the problem, identify whether essential information
is missing.

If it is, ask me no more than three specific questions.
Do not introduce silent assumptions.

6. Use the course learning sequence as a dialogue structure

The syllabus organises learning around:

conceptualisation
→ application
→ evidence analysis
→ perturbation of the problem
→ interpretation
→ defence of results

You can turn that sequence directly into a prompt:

I want to study [TOPIC].

Work with me in six stages:
1. conceptualisation;
2. application;
3. evidence;
4. change one assumption or input;
5. ask me to interpret what changes;
6. make me defend the conclusion.

This is more useful than requesting a long explanation.

7. Separate facts, assumptions, results and uncertainty

For quantitative problems:

Separate your response into:
- available data;
- assumptions;
- equations;
- calculation;
- result;
- physical interpretation;
- uncertainty;
- limitations.

This mirrors the course-wide assessment criteria: conceptual understanding, mathematical correctness, dimensional consistency, hydrological reasoning, physical interpretation, critical judgement and consideration of uncertainty.

8. Fluency does not imply truth

ChatGPT can produce polished but incorrect answers.

Watch especially for:

unit errors;
incorrect signs;
mixed temporal scales;
confusion between exceedance and non-exceedance;
methods used outside their assumptions;
fabricated data;
non-existent references;
non-existent R functions;
causal interpretations unsupported by evidence.

Try:

Audit your answer.

Look specifically for:
1. conceptual errors;
2. dimensional errors;
3. unstated assumptions;
4. physically implausible results;
5. omitted uncertainty;
6. references or functions you cannot verify.

9. AI is not a scientific authority

The syllabus is explicit: AI tools do not replace hydrological analysis and are not sources of scientific authority.

Verify important information using:

course material;
textbooks;
scientific papers;
official documentation;
R documentation;
original data;
independent calculations;
physical-consistency checks.

10. Declare AI use in practical assignments

Each practical assignment should include a brief declaration identifying at least:

tool used;
purpose;
component in which it was used;
verification procedure.

For example:

Tool: ChatGPT.
Purpose: support in debugging an R function for temporal aggregation.
Component: script 02_hydroclimate.R.
Verification: comparison with an independent calculation,
unit checks and consultation of the hydroTSM documentation.

“Used ChatGPT” is not an adequate declaration.

11. Study Mode and Socratic practice

Study Mode is useful for:

water balance;
Budyko;
infiltration;
hydrograph interpretation;
FDCs;
return period;
IDF curves;
rainfall-runoff relationships;
oral-assessment preparation.

For example:

I want to learn return period.

I do not want to memorise T = 1/p.
First diagnose my understanding.
Then give me an engineering-design situation.
Finally ask me about risk over the service life of the structure.

12. Three modes of use

Answer mode: “Calculate ET.”
Tutor mode: “Guide me to derive ET and check the units.”
Examiner mode: “Give me a problem, wait for my solution, then ask follow-up questions.”

For this course, prioritise tutor and examiner modes.

13. Free or Plus?

For IIO422, the Free plan is sufficient for most learning tasks.

Plus may become useful if you repeatedly need:

greater file capacity;
long debugging sessions;
higher tool availability;
longer or more complex analyses;
intensive use across several courses.

If your household budget is limited: start with Free and only pay for Plus after identifying a specific limitation that is genuinely affecting your work.

14. Five prompts worth saving

Concept

I want to learn [TOPIC].
First diagnose what I understand.
Then correct my gaps using an example from Chile.
Finish with a transfer question.

Problem solving

I will show you a problem and my attempt.
Do not solve it immediately.
Identify the first error, give me one hint, and wait.

Audit

Review the concept, equations, units, calculation,
interpretation, uncertainty and assumptions.

Oral practice

Give me a short oral assessment.
Ask one question at a time.
Use follow-up questions and correct me only at the end.

Error detection

Do not assume my method is correct.
Look for conceptual, numerical, methodological
and interpretative errors.

15. Final rule

Generative AI is permitted for learning activities and preparation of the practical assignments, but not during assessments. The syllabus also makes clear that being unable to explain a procedure included in a submission is evidence that the corresponding learning outcome has not been achieved.

The practical rule is therefore:

If you cannot explain, reproduce, modify and verify something that ChatGPT produced, you should not submit it as part of your work.

Sources

IIO422-Hydrology course syllabus, Universidad de La Frontera, 2026.
Baron, M. (2026). . The Koala News.
Tobor, J., & Sommer, T. (2026). . Hochschulforum Digitalisierung.
Vieriu, A. M., & Petrea, G. (2025). The impact of artificial intelligence (AI) on students’ academic development. Education Sciences, 15(3), 343.
Woon, A. (2025). . Higher Education Policy Institute.

Currency note: ChatGPT features, limits and prices change. Always check the official documentation.

How to Use ChatGPT to Learn Hydrological Modelling (II): conceptual understanding and critical thinking

Sun, 16 Aug 2026 00:00:00 +0000

Estimated reading time: 18-22 minutes

In Part I we looked at setting up ChatGPT, giving it context, writing clearer prompts and checking its answers. Now the goal changes.

Here ChatGPT is not an encyclopaedia. It becomes a tutor, examiner, critical interlocutor and case generator.

The theory component in IIO409 is designed precisely to reduce the value of memorising definitions and to increase the value of understanding, interpreting, diagnosing and arguing:

\[ Theory = 0.40\,ECT + 0.20\,LCA + 0.25\,DMC + 0.15\,PC \]

where ECT stands for Conceptual Theory Assessments, LCA for Critical Reading of Scientific Articles, DMC for Modelling Diagnosis through Case Studies, and PC for Scientific Participation in Class.

The implication is important: if you use ChatGPT only to obtain ready-made answers, you are using it in the least useful way for this assessment design.

1. Your goal is not to remember: it is to build a mental model

Suppose you are studying equifinality.

You can ask:

What is equifinality?

You will get a definition.

But a definition is not the same thing as understanding.

Try this sequence instead:

Explain equifinality with an example from a conceptual rainfall-runoff model.

Then:

Now give me two parameter sets that could produce similar hydrographs
but different internal states. I do not need real numbers; I want to understand the mechanism.

Then:

Why might this problem remain hidden if I look only at NSE or KGE?

Then:

Now give me one plausible but incorrect statement about equifinality.
Do not tell me why it is wrong until I try to criticise it.

Finally:

Change the problem: now the aim is not total discharge reproduction, but low-flow analysis.
Does the importance of equifinality change? Ask me first what I think.

This turns a static definition into a structure of reasoning.

2. The Socratic ladder

OpenAI’s Study Mode is based on interactive questioning, scaffolded explanations and checks of understanding. Even without any special mode, you can reproduce that logic in your own prompting.

Use five levels.

Level 1: explain

Explain the concept with an example from a Chilean catchment.

Level 2: retrieve

Stop the explanation and ask me five questions, one at a time.
Do not show the answer until I reply.

Level 3: challenge

Look for weaknesses in my replies.
Do not correct only the outcome: identify the conceptual error.

Level 4: transfer

Change one important condition of the problem and ask whether my reasoning still holds.

Level 5: defend

Act as a lecturer in an oral defence.
Question my assumptions and ask for a justification of each decision.

If you can answer successfully at level 5, your understanding is probably much stronger than if you can merely repeat a definition.

3. ECT (40%): Conceptual Theory Assessments

ECT tasks will prioritise interpretation and reasoning.

To prepare well, you should not simply ask ChatGPT for “exam questions”. Instead, ask it for the type of cognitive work the assessment values.

3.1 Interpreting results

Prompt:

Generate a short hydrological modelling case with:
- performance in calibration and verification;
- bias;
- one efficiency metric;
- one visible feature of the hydrograph.

Ask me three interpretation questions.
Do not give the solutions until I answer.

Then ask:

Evaluate my reply by separating:
- observation;
- inference;
- conclusion that is not yet justified.

This distinction is extremely useful. Many student errors occur when an observation is turned immediately into a causal claim.

For example:

“The model underestimates peaks.”

is an observation.

“Precipitation must be underestimated.”

is a hypothesis, not a necessary conclusion.

3.2 Comparing methodological alternatives

Try:

Compare two calibration strategies:
A) optimise NSE;
B) optimise a metric that gives greater weight to low flows.

Do not tell me which one is better.
Set up a use case and ask me which I would choose and why.
Then change the use case.

The important idea here is that a methodological decision depends on the purpose of the model.

3.3 Working with scenarios

I have a model calibrated in a wet period.
Create a dry-period verification scenario.
Ask me what problems I would expect and what diagnostics I would perform.

Then:

Give me an alternative explanation to mine.

Learning to produce alternative explanations is essential if you want to avoid overconfident conclusions.

3.4 Finding assumptions

Ask:

Give me an apparently correct methodology for calibrating a conceptual model,
but hide five implicit assumptions.
Do not flag them.
My task will be to identify them.

Then compare your list with ChatGPT’s and discuss any differences.

4. How to study each main content block in IIO409

4.1 Hydrological models and conceptual models

Do not memorise only classifications.

You should be able to answer:

Which process is each store trying to represent?
Which processes are absent?
Which observations could discriminate between two structures?
Which parts of the discharge behaviour depend on which model components?

Prompt:

Describe a catchment with climate, seasonality, snow/no snow, vegetation,
geology and discharge regime.

Do not choose a model for me.
Ask me what processes I would include in my conceptual model and why.
Then critique my representation.

A more demanding exercise:

Propose two different conceptual models that could reproduce the same discharge behaviour.
Ask me what extra data I would use to discriminate between them.

4.2 Hydrometeorological data

The question is not only “What data exist?”, but:

Are these data suitable for the modelling question?

Try:

Give me a hypothetical data set for a Chilean catchment with five quality problems:
missing dates, inconsistent units, bias, station change or temporal-resolution problems.

Do not tell me the problems. Give me clues and ask me to diagnose them.

Or:

Compare an in situ source, a satellite product and a reanalysis for precipitation.
Build the comparison in terms of:
- resolution;
- representativeness;
- biases;
- coverage;
- implications for calibration.

Then give me one case where the apparently most precise option is not the best.

4.3 Sensitivity: from “which parameter matters” to “why”

For Sobol analysis you should understand at least the conceptual difference between first-order effects and interactions.

Prompt:

Generate hypothetical Sobol results for four parameters.
Include first-order and total indices.

Do not interpret the table.
Ask me:
1. which parameter dominates;
2. which one shows interaction;
3. what this implies for calibration;
4. what I cannot conclude from these indices.

A deeper question:

Can a weakly sensitive parameter still be physically important?
Make me argue both for and against.

This helps you separate sensitivity from “physical importance”, which are not the same thing.

4.4 Calibration and verification

Study calibration as a problem of inference and decision, not merely “finding the best parameter values”.

Prompt:

Give me a case in which a calibration with PSO obtains an excellent objective value,
but the model is still not adequate.

Include enough clues for me to diagnose the problem.
Do not give me the solution yet.

You should be able to discuss:

objective function;
warm-up period;
dependence on the calibration period;
parameter bounds;
overfitting;
stability across periods;
multiple optima;
equifinality;
the relation between purpose and metric.

Make ChatGPT challenge you:

I am going to defend this claim:
"The parameter set with the best KGE is the true parameter set of the catchment".

Argue with me as a lecturer.
Do not accept claims without justification.

4.5 Uncertainty and GLUE

An uncertainty band does not speak for itself.

Prompt:

Create a conceptual GLUE example with:
- 10,000 simulations;
- an acceptance threshold;
- a percentage of behavioural simulations;
- a predictive band.

Ask me to interpret what each element means.
Then ask me which sources of uncertainty are NOT necessarily represented.

Another important question:

If I increase or reduce the GLUE threshold, what effects should I expect?
Do not answer: make me predict first.

4.6 Regionalisation

Prompt:

I have an ungauged catchment and three potential donor catchments.
Create climatic, topographic and hydrological-signature information for the four.

My task will be to choose a regionalisation strategy.
Then question my similarity criterion.

You need to understand that geographical proximity and hydrological similarity are not synonyms.

4.7 Climate change

Here the key is to think in terms of a modelling chain.

Prompt:

Build a conceptual chain from climate scenarios to future discharge:
climate model -> downscaling/bias correction -> meteorological variables -> hydrological model -> indicators.

Hide three sources of uncertainty.
Ask me to identify them and explain how they propagate.

Then:

Now change just one methodological decision in the chain.
Ask me how the conclusions might change.

5. LCA (20%): Critical Reading of Scientific Articles

ChatGPT can summarise papers. That is probably one of the least demanding ways to use it.

For LCA you need to go further.

Step 1: read first yourself

Before asking ChatGPT anything, identify:

the problem;
the question;
the hypothesis;
the data;
the method;
the results;
the main conclusion.

Then use ChatGPT to compare with your interpretation.

I am going to give you a paper.
Do not summarise it first.

Ask me these questions one at a time:
1. What is the scientific question?
2. What is the hypothesis?
3. What evidence could falsify it?
4. Which methodological decisions are critical?
5. Which limitations affect the conclusion?

Step 2: separate result from interpretation

Build a table with:
- observed result;
- authors' interpretation;
- another plausible interpretation;
- evidence needed to distinguish between them.

Step 3: search for assumptions

Identify five methodological assumptions in the paper.
For each one, indicate which conclusion might change if the assumption fails.

Step 4: formulate a new question

Based only on the paper, propose five possible follow-up research questions.
Order them from a small extension to a genuinely new conceptual question.

Then decide yourself which one is scientifically meaningful.

Step 5: prepare an oral presentation

Do not ask:

Make my slides.

Instead:

Help me identify the three essential scientific messages.
Ask me first what I think is most important before proposing them.

The presentation should be a consequence of your understanding, not a substitute for it.

6. DMC (25%): Modelling Diagnosis through Case Studies

This component is especially suitable for studying with ChatGPT because it can generate endless variations of a case.

Base prompt:

Act as an IIO409 lecturer.

Create a realistic rainfall-runoff modelling case with at least four methodological problems.
The problems may involve data, warm-up, calibration, verification,
objective function, sensitivity, parameters, uncertainty or interpretation.

Do not mark the errors.
Give me only the case and then ask:
1. which problems I detect;
2. which one is most serious;
3. what consequences it would have;
4. how I would correct it.

After you answer:

Do not give me your full solution yet.
Tell me only which important problem I failed to detect.

Finally:

Now provide your full diagnosis and compare it with mine.

Make the cases progressively harder

Beginner level:

calibration and verification in the same period;
incorrect units;
no warm-up period.

Intermediate level:

objective function inconsistent with the modelling aim;
parameters at the bounds;
climate-change analysis with an inconsistent baseline.

Advanced level:

good aggregate metric but structured errors;
parameter compensation;
inconsistency between internal states and discharge;
parametric uncertainty presented as total uncertainty.

7. PC (15%): Scientific Participation in Class

Participation is not the same thing as talking often. It means making a contribution that improves the discussion.

ChatGPT can help before class.

Suppose the topic is parameter identifiability.

Tomorrow we will discuss parameter identifiability.

Give me:
- three conceptual questions that could open a discussion;
- two debatable statements;
- one example in which high sensitivity does not guarantee identifiability;
- one counter-argument to each statement.

Do not memorise the questions. Choose one that you actually understand and rephrase it in your own words.

You can also use ChatGPT to prepare hypotheses:

Give me five possible explanations for a parameter that changes strongly between two periods.
Classify them as:
- data;
- structure;
- optimisation;
- non-stationarity.

A scientifically useful classroom contribution might be:

“If the parameter changes between periods, how do we distinguish weak identifiability from structural compensation within the model?”

That connects concepts and opens discussion.

8. Feynman technique with ChatGPT

A good test of understanding is to explain a concept in your own words.

Prompt:

I am going to explain GLUE as if I were teaching it to a classmate who is just beginning.

Do not interrupt me.
When I finish:
1. identify what is correct;
2. find ambiguities;
3. detect important missing ideas;
4. ask me one question that my explanation cannot yet answer.

Then rewrite the explanation yourself.

Do not ask ChatGPT to rewrite it for you.

9. The “predict before you ask” method

This is one of the best study strategies for hydrological modelling.

Before consulting ChatGPT, write your own prediction:

If I increase a parameter associated with storage, I expect that...

Then ask:

Evaluate my prediction.
Do not focus only on whether the direction is correct.
Discuss under which conditions it could fail.

This prevents the passive habit of allowing ChatGPT to think first every time.

10. Build your own bank of mistakes

Each time you answer something incorrectly, record:

Concept:
My answer:
Error:
Why I got it wrong:
Correct principle:
New example:

After several weeks, ask:

Here is my error log.
Do not explain the topics again.
Identify patterns and create five new questions targeted specifically at my weaknesses.

This turns ChatGPT into an adaptive practice tool.

11. Master prompt for preparing an ECT

Act as a Civil Engineering lecturer in Hydrological Modelling.

Topic: [TOPIC].

I want to prepare for a conceptual assessment.
Do not test memorisation of definitions.

Generate five questions with increasing difficulty:
1. interpretation;
2. methodological comparison;
3. cause-effect;
4. diagnosis;
5. transfer to a new scenario.

Ask me one question at a time.
Wait for my answer.
Assess the reasoning, not only the conclusion.
When my answer is incomplete, use a hint before giving the solution.
At the end, identify my two main conceptual weaknesses.

12. Master prompt for DMC

Create a realistic hydrological diagnosis case for IIO409.

Include information about:
- data;
- model;
- warm-up;
- calibration;
- verification;
- objective function;
- parameters;
- results;
- uncertainty.

Introduce between three and five methodological problems of different severity.

Do not label them.
Ask me to:
1. identify them;
2. prioritise them;
3. explain the consequences;
4. propose corrections;
5. defend one alternative.

Then question my diagnosis as in an oral defence.

13. What not to do when studying theory with ChatGPT

Avoid turning your studying into:

Summary -> passive reading
Flashcards -> repetition without context
Automatic answers -> false confidence
AI-generated essay -> no practice of reasoning
Unquestioned explanations -> learning mistakes

The key question is always:

Can I apply this idea in a new case without ChatGPT telling me what to do?

If the answer is no, you are still studying too passively.

14. Before an assessment

Try this 45-minute routine:

10 min: retrieval

Without ChatGPT, write down everything you remember about the topic.

15 min: Socratic questioning

Use ChatGPT to identify gaps.

10 min: new case

Ask for a problem you have not seen before.

5 min: oral explanation

Explain it out loud without looking.

5 min: error log

Write down exactly what you did not understand.

This is far more useful for IIO409 than asking ChatGPT for “a short summary for the test”.

15. The goal: learn to think like a modeller

A good modeller is not defined by the ability to recall the longest definition.

A good modeller asks:

What does this model represent?
What does it not represent?
What evidence supports this decision?
What alternatives exist?
What assumption am I making?
How might I be wrong?
Which observation would distinguish between competing explanations?
Does the conclusion depend on the purpose?
What uncertainty remains?

ChatGPT can help you practise those questions again and again.

But only if you use it deliberately: make it question you, contradict you, change the conditions and demand that you justify your choices.

In Part III we will take the same logic into the practical side of the course: base R, vibe coding, reproducibility, debugging, sensitivity, calibration, uncertainty and preparation for the practical challenges.

Learning | Dr. Mauricio Zambrano-Bigiarini

How to use ChatGPT to learn Hydrology - Part I: configuration, context and useful prompts

Why this series of tutorials?

1. The fundamental rule: tutor, not autopilot

2. Configure the context that actually matters

3. Connect ChatGPT with the Learning Outcomes

4. The anatomy of a good prompt

Role

Context

Task

Constraints

Output

5. Make the model recognise missing information

6. Use the course learning sequence as a dialogue structure

7. Separate facts, assumptions, results and uncertainty

8. Fluency does not imply truth

9. AI is not a scientific authority

10. Declare AI use in practical assignments

11. Study Mode and Socratic practice

12. Three modes of use

13. Free or Plus?

14. Five prompts worth saving

Concept

Problem solving

Audit

Oral practice

Error detection

15. Final rule

Sources

How to Use ChatGPT to Learn Hydrological Modelling (II): conceptual understanding and critical thinking

1. Your goal is not to remember: it is to build a mental model

2. The Socratic ladder

Level 1: explain

Level 2: retrieve

Level 3: challenge

Level 4: transfer

Level 5: defend

3. ECT (40%): Conceptual Theory Assessments

3.1 Interpreting results

3.2 Comparing methodological alternatives

3.3 Working with scenarios

3.4 Finding assumptions

4. How to study each main content block in IIO409

4.1 Hydrological models and conceptual models

4.2 Hydrometeorological data

4.3 Sensitivity: from “which parameter matters” to “why”

4.4 Calibration and verification

4.5 Uncertainty and GLUE

4.6 Regionalisation

4.7 Climate change

5. LCA (20%): Critical Reading of Scientific Articles

Step 1: read first yourself

Step 2: separate result from interpretation

Step 3: search for assumptions

Step 4: formulate a new question

Step 5: prepare an oral presentation

6. DMC (25%): Modelling Diagnosis through Case Studies

Make the cases progressively harder

7. PC (15%): Scientific Participation in Class

8. Feynman technique with ChatGPT

9. The “predict before you ask” method

10. Build your own bank of mistakes

11. Master prompt for preparing an ECT

12. Master prompt for DMC

13. What not to do when studying theory with ChatGPT

14. Before an assessment

10 min: retrieval

15 min: Socratic questioning

10 min: new case

5 min: oral explanation

5 min: error log

15. The goal: learn to think like a modeller

Recommended references and further reading