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1. Developing higher-order scientific literacy and experimental design skills.

2. Cultivating critical AI literacy and metacognition.

3. Fostering autonomy through digital equity and low-resource access.

4. Enhancing professional career awareness and communication skills.

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Problem-Based Learning (PBL): Students begin with an authentic scientific problem and must design their own experiment to solve it. Rather than receiving instructions, they investigate, make decisions, test ideas, and refine their thinking through inquiry.

Inquiry-Based Learning (IBL): The ITP follows the scientific inquiry process: Asking questions, Formulating hypotheses, Designing investigations, Collecting and analyzing data, Drawing conclusions

Constructivist Learning Theory: students build new understanding from their own experiences. Through virtual experimentation, comparison with AI-generated expert approaches, and reflection, learners actively construct scientific knowledge

AI-Enhanced Learning Framework : Generative AI is used as a cognitive tool rather than an answer provider. Students critically assess AI-generated responses, examine strengths and limitations, and develop responsible AI literacy skills necessary for modern scientific practice.

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Ensuring Human Agency during the Use of AIED

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Ensuring Human Agency during the Use of AIED

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The learning sequence begins with students independently designing an experiment to answer a scientific question before accessing any AI support.

After conducting their virtual experiments, students use generative AI to simulate how a scientist from a relevant discipline might approach the same problem.

Students are required to make independent decisions about whether the AI-generated suggestions would improve their experimental design. This process develops critical evaluation skills and reinforces the idea that human judgment remains essential when working with AI-generated information.

Responsible and Ethical AI Use: Verifying AI-generated information against scientific evidence; Recognizing potential inaccuracies and biases; Understanding the limitations of AI in scientific inquiry;  Maintaining academic integrity by documenting how AI was used during the learning process

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1. Students will be able to design a structured, controlled experimental protocol and answer a specific scientific question without relying on step-by-step instructions.

2. Students will be able to manipulate digital parameters (using PhET/NB Lab) to collect, log, and organize realistic data.

3. Students will be able to critically compare their own intuitive experimental designs against an AI-generated expert protocol, identifying specific procedural missing links and explaining why those differences exist.

4. Students will be able to evaluate and articulate the ethical implications of using AI in modern scientific research, discussing themes like intellectual property, the loss of human intuition, and accountability in automated discoveries.

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Introduction (Evolution in Science, General Presentation on AI)

Students are exposed to the evolution of technology from classical dictionary search to the use of generative AI.

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Problem Launch

Students receive a real scientific question (e.g., "What factors affect a pendulum's period?") without any prior reading.

They design a full experiment (hypothesis, variables, procedure, data table)

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Virtual Lab Simulation (Student Run)

Students input their experimental design into virtual lab software (PhET, NB Lab or LabXchange).

They "run" their experiment in the simulation, collect real-time data, and record results.

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Gen AI Simulation (Scientist Comparison)

Students use ChatGPT/DeepSeek/Gemini to simulate how a historical scientist (Galileo, Mendel, Lavoisier) would design the same experiment.

They compare their virtual lab results with the scientist's predicted approach.

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Students complete a structured "Gap Analysis" table comparing their design (from Lesson 3) with the scientist's design (from Lesson 4).

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Career & Ethics Discussion

Students reflect on why the scientist's design was different, discuss limitations of AI (bias, hallucination, lack of physical reality), evaluate the virtual lab's accuracy, and write a mock "funding proposal" as a lab director.