Presentation Round 2: From Literature to a Research Plan

Overview

Round 2 is where your project moves from understanding a research area to designing a study. In Round 1, your goal was to understand a cognitive phenomenon: how it has been defined, what evidence exists, where findings agree or disagree, and what important questions remain open. In Round 2, your goal is to turn one of those open questions into a testable first version of your research project.

This is not another literature review. Prior work now serves a different purpose: it helps you determine what has already been tested, how it has been operationalized, what remains missing, and how your own study can make a meaningful next step.

These guidelines apply to the Round 2 group presentation listed in the course syllabus; each presentation round runs over two class sessions.

Goals of Round 2

By the end of your presentation, the audience should understand:

• What is the question?
• Why does it matter?
• How has prior work approached it?
• How will you test it?
• What results would be informative?
• Where does the design still need refinement?

From Round 1 to a Research Gap

Briefly remind the audience of the one or two conclusions from Round 1 that motivate your next step. A useful transition is what do we already know → what remains unresolved → what should we test next. Your Round 2 question should be narrower and more testable than the broad topic reviewed in Round 1.

Find Your Nearest-Neighbor Studies

Identify approximately 2–4 studies that are closest to the study you now want to conduct. These are not necessarily the most famous papers in your topic. They are the studies whose research question, stimuli, task, dataset, computational approach, or evaluation method most closely resemble what you plan to do.

For each study, focus on question → operationalization → critical comparison → finding → limitation. Do not give another complete paper presentation. The goal is to understand the existing design space and locate the smallest meaningful step beyond it.

Example to Explore: Finding the Nearest Prior Work

The Stanford CS197C project proposal asks students to identify the work most similar to their proposed project and then articulate the meaningful change that distinguishes the new study. The useful question is not simply “Has anyone done this exact project?” but: what is the closest existing study, and what scientifically meaningful thing are we changing?

Identify the Meaningful Difference

A good project usually changes one important assumption, variable, population, modality, task, or comparison.

This allows the new study to distinguish between two explanations that the original design could not separate.

Operationalize the Research Question

A research question becomes experimentally useful only when its key concepts can be connected to observable data. A useful structure is cognitive construct → stimulus / data → task → response → measure. For your project, clarify two things.

What Is the Construct?

What cognitive ability, representation, process, or distinction are you trying to understand?

What Is the Evidence?

What will participants or models observe, and what response will they produce? Consider:

• Stimulus / data: words, images, videos, narratives, interactive environments, or existing datasets.
• Task: classify, choose, predict, compare, recall, interact, or generate.
• Response: choice, text, probability, action, trajectory, or representation.
• Measure: accuracy, reaction time, choice probability, similarity structure, error pattern, or another metric.

Most importantly, ask why this measure should tell us something about the construct we care about. A task producing a numerical score does not automatically mean that the score measures the intended cognitive ability.

Example to Explore: From an Idea to an Operational Plan

The Stanford Undergraduate Research project proposal guidelines show how a research proposal connects what the project asks, why it matters, and how the study will answer it. When reviewing your own design, ask whether the chain research question → method → evidence is clear.

For AI Evaluation: Make the Interface Explicit

When your project evaluates AI, operationalization requires an additional step: specify exactly what information the model receives, how the task is presented, and how its response becomes evidence. A useful structure is input → prompt / adaptation → model → output → scoring. Clarify:

• Input: what information does the model receive? Text, image, video, demonstrations, interaction history, or other context?
• Prompt / adaptation: how is the task presented? What instructions, examples, or response constraints are provided?
• Output: what counts as the model’s response? A multiple-choice answer, probability, generated text, action, trajectory, or representation?
• Scoring: how is that output converted into a measurement? Accuracy, similarity, likelihood, human rating, behavioral agreement, or another metric?
• Model conditions: what must remain controlled? Model / version, decoding settings, number of repeated runs, and other settings that may affect the result.

The full inferential chain should therefore be explicit: cognitive question → human / AI input → task interface → response → measure → inference.

Examples to Explore: Standardized AI Evaluation

• Stanford CRFM — Holistic Evaluation of Language Models (HELM) emphasizes standardized scenarios, adaptation procedures, prompts, and metrics, reminding us that model performance cannot be interpreted independently of how the task is presented.
• The NIST Generative AI Evaluation Plan explicitly specifies task definitions, task conditions, input/output formats, metrics, and evaluation protocols. An AI benchmark is not just a dataset and a score; the full evaluation interface must be defined.

Develop a Preliminary Experimental Design

Round 2 does not require a final, perfect design. It requires a first version that is specific enough to test, critique, and improve. Your preliminary design should make clear:

• the research question and prediction;
• the conditions or comparison groups;
• the key controls or baselines;
• a basic analysis or evaluation plan.

You do not need every parameter finalized. What matters is that someone else can understand what will be compared and why that comparison answers your question.

Make the Critical Comparison Explicit

Every useful study has a comparison that does the main inferential work. Ask: what comparison would distinguish the explanation we care about from an alternative explanation? Examples include:

• Condition A vs. Condition B;
• humans vs. AI on the same task;
• a model with information X vs. a model without information X;
• observed performance vs. a baseline or null model.

You should also be able to state the expected qualitative pattern:

• If H1 is supported: A > B.
• If H1 is not supported: A ≈ B.

If the critical comparison is unclear, the design probably needs further refinement.

Example to Explore: What a Computational Project Proposal Should Specify

The Stanford CS224S course project guidelines connect the task, approach, and evaluation so that the reader can see what will actually be tested.

Check Feasibility and Scope

Before moving forward, ask:

• Can the required data or stimuli be obtained?
• Can the study be completed within the semester?
• Are participant recruitment, ethics, model access, and computational cost manageable?
• What is the minimum viable study that can still answer the question?

A focused study that cleanly answers one question is better than an ambitious design with no clear inference.

Show and Structure Your Study

A strong Round 2 presentation should make the research logic easy to follow and concrete enough to evaluate. Whenever possible, include one slide that shows the study as a pipeline, for example stimuli / data → task → human / AI responses → measures → comparison, or Condition A vs. Condition B → critical difference → supports / challenges hypothesis. Someone unfamiliar with your project should be able to understand what goes in, what happens, what comes out, and what gets compared.

Show Something Concrete

You do not need final results in Round 2. However, when possible, show an early piece of the actual study:

• candidate stimuli or an example trial;
• a questionnaire, prototype, or interface;
• sample model outputs;
• a baseline implementation;
• a small pilot or preliminary visualization.

The goal is to make the design concrete and expose practical problems early.

Organize the Presentation Around the Research Logic

There is no mandatory slide template, but a strong presentation will often follow question → existing work → gap → design → evidence → open questions:

1. Research question: what exactly are you trying to test?
2. Nearest existing work: what are the closest prior studies or benchmarks?
3. Gap: what important question remains unresolved?
4. Preliminary design: how will you test it?
5. Evidence: what comparison, metric, or result pattern would answer the question?
6. Open questions: what still needs feedback or refinement?

Spend less time repeating the literature from Round 1 and more time showing what you plan to test and why the design can answer the question.

Ask for Targeted Feedback

Round 2 should function partly as a design review. End with 2–4 specific unresolved questions that would help improve the next version of the study. For example:

• Are these stimuli isolating the intended variable?
• Is the current baseline appropriate?
• Should this factor be manipulated or controlled?
• Does the proposed measure distinguish the competing hypotheses?

Avoid ending with

Any suggestions?

Ask questions that can lead directly to a design decision.

Example to Explore: How a Project Progresses

The Stanford CS341 project information and checkpoints show how research progresses from problem and related work → methodology and experimental setup → preliminary results and current challenges. A good Round 2 presentation should leave the audience with both a clear understanding of the proposed study and a clear sense of what needs to happen next.

Practical Reminders

• Build on the nearest existing work.
• Make one meaningful scientific change.
• Keep the construct distinct from the measure.
• Make the critical comparison explicit.
• Use appropriate controls or baselines.
• Consider alternative explanations.
• Keep the study feasible and pilot early.
• Ask for targeted feedback.

Before You Present: Checklist

Question and Gap

• Can we state the research question clearly?
• Do we know the closest existing studies?
• Can we explain what remains unresolved and why it matters?

Design

• Is the construct operationalized clearly?
• Are the inputs or stimuli, task, responses, and measures explicit?
• Is the critical comparison clear?
• Are the main controls or baselines included?

Evidence

• Do we know what result would support or challenge the hypothesis?
• Have we considered at least one alternative explanation?

Feasibility

• Can the minimum viable study be completed this semester?
• Do we know what participants, data, models, tools, or approvals are required?

Presentation

• Can someone understand the study from the experimental diagram?
• Have we shown something concrete where possible?
• Have we prepared specific questions for feedback?

What You Should Have After Round 2

By the end of Round 2, your project should have:

1. A focused research question.
2. The nearest existing work.
3. A clearly motivated research gap.
4. A preliminary, testable design.
5. A critical comparison and evaluation plan.
6. Expected result patterns and alternative explanations.
7. Specific unresolved design decisions.

These are not only preparation for the next stage of the project. They should also begin to form the backbone of your final paper:

• Research question and gap → Introduction.
• Nearest existing work and unresolved issues → Related Work.
• Operationalization, experimental design, and evaluation plan → Methods.
• Expected result patterns → the analyses and figures you will eventually need in Results.
• Alternative explanations and unresolved decisions → Discussion, Limitations, and Future Directions.

Your Round 2 presentation should therefore leave you with a first answer to what are we asking, why is it still unresolved, how will we test it, and what evidence would answer the question. These materials will become the foundation for the next stage, where you will refine the design, implement the task or benchmark, pilot the study, establish baselines, and begin collecting preliminary results.

References

• Stanford Undergraduate Research — Detailed Project Proposal Guidelines: a general guide to connecting the research question, significance, approach, and project scope.
• Stanford CS197C — Project Proposal: useful for thinking about nearest-neighbor work, meaningful differences, and project contribution.
• Stanford CS224S — Course Project: useful for checking whether the task, data, approach, and evaluation are sufficiently explicit.
• Stanford CS341 — Project Information and Checkpoints: a useful example of how a project progresses from problem definition and related work to methodology, implementation, and preliminary results.
• Stanford CRFM — Holistic Evaluation of Language Models (HELM): useful for AI-focused projects; pay attention to scenarios, prompts and adaptation, model outputs, and evaluation metrics.
• NIST — Generative AI Evaluation Plan: useful for thinking carefully about task conditions, input/output formats, metrics, and evaluation protocols.