In Lesson 4, you encountered sustainability indicators in the context of bioenergy. Lesson 7 returns to sustainability indicators and applies them directly to your final project topic.

Sustainability indicators are used to make selected conditions, impacts, and changes visible. An indicator can support comparison, evaluation, monitoring, or decision-making. Indicator selection is also an ethical process. The selected indicators determine which benefits, burdens, risks, stakeholders, timescales, and system functions receive attention. Other concerns may remain difficult to measure or may disappear from the analysis.

During this one-week lesson, you will use generative AI as a structured analytical assistant. You will provide the same project context and ethical criteria to two or three AI models, compare the indicator sets they propose, and use Ethics Matrices A, B, and C to evaluate and prioritize the results. The AI models will generate possibilities. You remain responsible for deciding which indicators are suitable, how they should be defined, and why they belong in the final project.

Central Question: How can ethics matrices and generative AI be used together to select sustainability indicators without surrendering human judgment to the AI system?

Learning Objectives

• Explain why the selection of sustainability indicators is an ethical and methodological choice.
• Use Ethics Matrices A, B, and C as criteria for generating, screening, and prioritizing indicators.
• Construct a project brief and common prompt suitable for comparison across AI models.
• Compare areas of convergence, divergence, omission, and ambiguity in outputs from two or three AI systems.
• Distinguish a broad sustainability topic from an operational indicator that can be observed, measured, or assessed.
• Select and justify a balanced portfolio of sustainability indicators for the final project.
• Document AI use and retain responsibility for verification, interpretation, and final judgment.

Using the Ethics Matrices as Selection Criteria

The matrices are not being used to produce a mechanical score. Each matrix provides a different set of questions for evaluating proposed indicators.

Using Penn State AI Studio

Use Penn State AI Studio for the assignment. Begin separate conversations with two or three available AI models so that the outputs remain independent. Upload Ethics Matrices A, B, and C to each conversation, along with the same project-topic brief and the same initial prompt.

Access: Penn State AI Studio

Guidance: Penn State Best Practices for Using AI Tools

Do not upload confidential, proprietary, personally identifying, or otherwise restricted information. Use a project description that contains only information appropriate for course work.

One-Week Workflow

From Sustainability Topics to Operational Indicators

AI systems frequently propose broad themes such as equity, biodiversity, community well-being, affordability, or resilience. These are important domains, but they are not yet operational indicators.

A usable final indicator should specify:

• a clear indicator name and operational definition;
• a unit, calculation, scale, threshold, or assessment method;
• the geographic and temporal boundary;
• the relevant stakeholder, system function, benefit, burden, or risk;
• the connection to one or more ethics matrices;
• a plausible source of evidence or data;
• and an important limitation or possible source of misinterpretation.

The final portfolio should be manageable, sufficiently diverse for the project, and free of unnecessary duplication. The portfolio should also be evaluated as a whole. Several individually useful indicators can still produce an ethically narrow set if they consistently omit a stakeholder group, timescale, or category of impact.

Principles for the AI Comparison

• Use the same project brief and initial prompt with each model.
• Run the models in separate conversations so one model's output does not influence another model.
• Treat model agreement as convergence, not proof that an indicator is correct.
• Treat model disagreement as information about ambiguity, framing, or differing interpretations.
• Verify factual claims, measurement methods, and proposed data sources before using them.
• Do not treat AI-generated citations as reliable until the sources have been independently confirmed.
• Document consequential follow-up prompts and revisions.
• Retain responsibility for the final indicator definitions and priorities.

Assignment 7

Assignment 7: AI-Assisted Ethical Selection and Comparison of Sustainability Indicators

For Assignment 7, you will use two or three AI models to propose sustainability indicators for your final project topic. You will compare the outputs, use Ethics Matrices A, B, and C to evaluate them, and select a final portfolio of six to eight indicators.

The submission will include:

• the common project brief and base prompt;
• a comparison of the AI-generated indicator sets;
• the final portfolio of six to eight operational sustainability indicators;
• a concise explanation of how each selected indicator relates to the ethics matrices;
• and a comparative reflection on model performance, omissions, rejected indicators, verification, and human judgment.

Complete instructions, formatting requirements, and submission links are provided in Canvas.

Readings and Materials

Lesson 7 does not introduce a new survey of sustainability-indicator literature. Return to the bioenergy sustainability-indicator material from Lesson 4 and use that case as a reminder that indicator sets are constructed for particular systems, decisions, stakeholders, and definitions of sustainability.

• Review the sustainability-indicator material assigned in Lesson 4.
• Review Ethics Matrices A, B, and C and the course guides for using them.
• Review the Lesson 7 page on indicator selection and AI comparison.
• Consult Penn State's AI Studio and best-practices guidance as needed.

Main Point

Generative AI can expand the range of possible indicators and make the first stage of indicator development more manageable. AI cannot determine which values should govern a project, whose interests should receive priority, which tradeoffs are acceptable, or what counts as an adequate representation of sustainability. The ethics matrices provide criteria for examining those questions, while comparison across models makes the limits and variability of AI-generated analysis visible.

Returning to Sustainability Indicators

In Lesson 4, you encountered sustainability indicators in the context of bioenergy. That case showed that sustainability cannot be represented by a single measure. Greenhouse-gas emissions may be important, but a bioenergy system can also affect land use, biodiversity, water, food security, labor, local livelihoods, public participation, and long-term resource conditions.

Lesson 7 applies the same basic problem to your final project topic. You will identify a set of indicators that can help evaluate the system, policy, technology, program, or intervention you are studying. The goal is not to find a universal list. The goal is to construct a project-specific portfolio that makes the most consequential conditions, benefits, burdens, risks, and uncertainties visible.

Central Question: What should count as evidence of sustainability for this project, and whose interests will the selected indicators make visible?

What Is a Sustainability Indicator?

A sustainability indicator is a defined measure, observation, ratio, index, threshold, or structured qualitative assessment used to represent some aspect of system condition, performance, impact, risk, or change. An indicator reduces a complex issue to a form that can be monitored, compared, discussed, or used in decision-making.

That reduction is useful, but it is never neutral. Every indicator selects some information and leaves other information outside the frame. The definition, unit, boundary, time period, comparison basis, and intended user all affect what the indicator means.

Domain, Goal, Indicator, and Target

These terms are often used interchangeably, especially in AI-generated lists. They perform different functions.

A phrase such as “equity,” “biodiversity,” or “resilience” identifies a domain. It becomes an indicator only after the relevant condition, population, boundary, method, and basis for interpretation are specified.

What Makes an Indicator Operational?

An operational indicator is specific enough that another person could understand what is being assessed and how the assessment would be carried out. Numerical measurement is not always required. A structured qualitative assessment can be appropriate when its categories, evidence requirements, and interpretive method are explicit.

Operational detail should be proportional to the project. At this stage, you are not required to build a complete monitoring system. You do need enough specificity to show that the proposed indicator could be used meaningfully.

Example: Moving from a Broad Topic to an Indicator

Each revised example still requires judgment. The project region must be defined, the service or output must be specified, and the quality of participation must be interpreted. Operationalization makes those choices visible.

Context and System Boundaries

An indicator that is useful in one project may be misleading in another. Relevance depends on the project purpose, technology, location, scale, stage of development, affected population, policy setting, available evidence, and time horizon.

System boundaries are especially important. A greenhouse-gas indicator might include only direct operational emissions, all life-cycle emissions, changes relative to a baseline, or avoided emissions compared with another scenario. Those are different indicators. A local-jobs indicator might include construction labor, permanent employment, contractors, supply-chain employment, or some subset. The boundary determines which benefits and burdens appear.

Questions About Context

• What decision or evaluation will the indicator support?
• At what geographic scale does the relevant effect occur?
• What project stage and time period matter?
• Which baseline or comparison is appropriate?
• Which effects occur outside the immediate project boundary?
• What evidence is realistically available?

Selecting an Indicator Portfolio

For the final project, you will select a portfolio rather than a single definitive indicator. A portfolio is a deliberate set of measures and assessments intended to represent the most important dimensions of the project.

A strong portfolio should represent more than one category of impact, include multiple stakeholder perspectives, capture relevant short- and long-term effects, avoid unnecessary duplication, and remain plausible to assess. The portfolio should also make important tradeoffs visible. Indicators that all move in the same direction or serve the same stakeholder may provide a narrow picture even when each indicator is individually well defined.

Indicator Selection as an Ethical Choice

Indicators influence attention. What is measured may become easier to manage, compare, fund, regulate, or defend. What is not measured may be treated as secondary, uncertain, or outside the decision.

The selection process therefore raises ethical questions. Who decides what counts as sustainability? Which stakeholders are represented? Which harms and benefits are quantified? Which effects are treated as indirect? Which time horizons receive priority? Which values are converted into proxies, scores, or thresholds?

Indicators can also be rhetorical. A project may appear sustainable when evaluation is limited to favorable metrics. A narrow indicator can be accurate within its boundary and still support a misleading overall claim. Ethical selection requires attention to both the quality of individual indicators and the pattern created by the set.

Returning to the Lesson 4 Bioenergy Case

The bioenergy material in Lesson 4 provides a useful model for this assignment. Bioenergy sustainability could not be evaluated through greenhouse-gas emissions alone. Land use, biodiversity, water, food security, labor, economic development, governance, and social participation changed the meaning of the assessment.

The same transfer should occur in your final project. Begin with the issues that appear most obvious, then ask what a single-issue approach would miss. Consider how geographic scale, system boundary, stakeholder position, and time horizon alter the apparent sustainability of the project.

Preparing for the AI-Assisted Search

Before asking an AI model to propose indicators, define the project context. A vague topic will produce generic lists. A grounded project brief gives the models a better basis for generating plausible candidates and gives you a consistent basis for comparison.

1. What system, policy, technology, program, or intervention is being evaluated?

2. What decision or project question should the indicators help address?

3. What is the geographic boundary?

4. What is the relevant time horizon or project stage?

5. Who are the principal stakeholders?

6. What environmental, social, economic, technical, and governance concerns are already known?

7. What benefits, burdens, risks, and uncertainties may be distributed unevenly?

8. What evidence is realistically available?

9. What important concerns may resist simple quantification?

Main Point

A sustainability indicator is a deliberately constructed representation of part of a system. A useful indicator must be operational, contextually appropriate, ethically defensible, and interpreted within its limitations. The next page explains how to use multiple AI models and Ethics Matrices A, B, and C to generate, compare, and refine a project-specific indicator portfolio.

Using AI as a Structured Analytical Assistant

Part 1 established that sustainability indicators are deliberately constructed representations of system conditions, impacts, risks, and changes. Part 2 provides a method for using generative AI to widen the search for plausible indicators and then using Ethics Matrices A, B, and C to evaluate, compare, and prioritize them.

The AI systems are not being asked to make the final decision. Their role is to generate possibilities, locate publicly available sources, propose operational definitions, and expose alternative ways of framing the project. Your role is to verify the information, compare the models, identify omissions and embedded assumptions, and select the final indicator portfolio.

The Two-Round Workflow

The assignment uses two rounds. The first round is an open search for plausible indicators. The second round uses the ethics matrices to analyze and prioritize a common candidate pool. Separating the rounds helps make the comparison more meaningful.

Step 1: Prepare a Common Project Brief

Begin with a concise description of the final project. Use the same brief with every AI model so that differences in the outputs are not caused by different background information.

The brief should identify:

• the project topic and the decision, technology, policy, program, or intervention being evaluated;
• the geographic boundary and relevant time horizon;
• the project stage or implementation context;
• the principal stakeholders;
• known environmental, social, economic, technical, and governance concerns;
• known constraints, uncertainties, and data limitations;
• the intended use of the final indicator portfolio.

A brief of approximately 150 to 250 words should be sufficient. Avoid asking the AI to invent the project context. State what is known and identify what remains uncertain.

What You May Upload and What You Must Not Upload

You may upload the course-provided Ethics Matrix A, Ethics Matrix B, and Ethics Matrix C files to the Penn State approved AI platform. You may also paste or upload your own non-sensitive project brief, public reports, public datasets, and publicly accessible links.

When in doubt, do not upload the material. Describe the relevant issue in general terms or rely on a public source instead.

Step 2: Open Independent Model Conversations

Use Penn State AI Studio and select two or three different AI models. Where possible, choose models from different model families. Begin a new conversation for each model and keep the conversations independent.

Access: Penn State AI Studio

Use the same project brief and initial prompt with every model. You may ask follow-up questions, but retain a record of consequential follow-up prompts so that the process can be compared and documented.

Step 3: Use Public Web Sources to Ground the Search

The models may search the public web when that capability is available. You may also conduct your own web searches and paste links into the conversation. Useful sources may include government agencies, international organizations, professional standards, peer-reviewed research, public datasets, industry reports, community plans, and established indicator frameworks.

Ask the model to distinguish between an indicator it generated independently and an indicator adapted from a published framework. Require links for factual claims and proposed data sources. A link is a starting point for verification, not proof that the claim is correct.

• Open the cited or linked source and confirm that it exists.
• Check that the source actually supports the proposed definition or method.
• Prefer primary or authoritative sources when available.
• Record the source used to define or justify each final indicator.
• Reject fabricated citations, broken links, or unsupported claims.

Round 1: Generate Plausible Candidate Indicators

Ask each model to propose approximately 10 to 12 candidate indicators. The purpose of this round is breadth. The models should search for plausible measures and operationalize them, but they should not make the final selection.

Review each output for generic language, duplicated indicators, implausible measurement methods, missing stakeholders, and unsupported claims. Do not ask one model to summarize the other models. Preserve the independence of the initial outputs.

Step 4: Create a Common Candidate Pool

Combine the strongest and most distinct candidates from the model outputs into a common pool of approximately 12 to 15 indicators. Merge obvious duplicates, but preserve meaningful differences in definition, boundary, stakeholder focus, or measurement method.

The common pool is important because every model will evaluate the same candidates in Round 2. This makes the ethical comparison clearer. Otherwise, differences in model rankings could simply reflect differences in the lists they originally generated.

Round 2: Upload and Apply Ethics Matrices A, B, and C

Upload the three course-provided matrix files to each model conversation. Instruct the model to read the matrices as analytical frameworks. Then provide the same consolidated candidate pool and the same Round 2 prompt to every model.

Ethics Matrix A: Integrity and Evidence

• Is the indicator clearly and consistently defined?
• Can the proposed evidence or data be verified?
• Is the measurement or assessment method valid and transparent?
• Are assumptions, uncertainty, and limitations disclosed?
• Could the indicator be used or communicated in a misleading way?

Ethics Matrix B: Broader Impacts and Justice

• Which benefits, burdens, risks, and public consequences does the indicator reveal?
• Which stakeholders are represented, and which remain invisible?
• Does the indicator address distributive, procedural, or intergenerational justice?
• Does it make policy, transformation, risk, or precaution visible?
• What broader impact would remain outside the indicator?

Ethics Matrix C: Embedded Choices in Indicator Design

• What boundary, category, proxy, or definition is built into the indicator?
• Which assumptions are required to calculate or interpret it?
• What is excluded or treated as fixed?
• Does the indicator privilege one stakeholder, timescale, or outcome?
• How could a reasonable alternative definition change the result?

The matrices should guide qualitative analysis rather than produce an automatic numerical score. A model may rank or group indicators, but every priority should be accompanied by a reason tied to the matrices and the project context.

Step 5: Compare the Model Analyses

Compare both the discovery outputs and the matrix-based analyses. Agreement is useful, but it is not proof that an indicator is valid. Disagreement may reveal ambiguity in the project, the indicator definition, the ethics matrices, or the models themselves.

Step 6: Select and Refine the Final Portfolio

Select six to eight final indicators. Revise the names, definitions, boundaries, methods, and limitations as needed. The final portfolio should be your synthesis, not a copied list from one model.

The final portfolio should:

• fit the specific project and decision context;
• contain operational indicators rather than broad themes;
• collectively engage Ethics Matrices A, B, and C;
• represent multiple stakeholder interests and system functions;
• address relevant short- and long-term effects;
• make significant tradeoffs, uncertainties, and burdens visible;
• avoid unnecessary duplication;
• use plausible evidence or data sources;
• state important limitations and risks of misuse.

Step 7: Verify Before Submitting

AI output is not evidence. Before using a proposed indicator, verify the factual claims and the source material needed to define or apply it.

• Confirm the definition, unit, formula, or assessment method.
• Confirm that cited frameworks, standards, and datasets exist.
• Check whether the proposed data are actually public and available at the stated scale.
• Distinguish a published threshold from a model-generated suggestion.
• Check whether the indicator measures the intended condition or only a proxy.
• Revise claims that exceed what the evidence can support.

Step 8: Document the AI Process

Retain the following materials for Assignment 7:

• the common project brief;
• the Round 1 and Round 2 base prompts;
• the model names used;
• the initial candidate outputs;
• the consolidated candidate pool;
• the matrix-based analyses;
• consequential follow-up prompts;
• the comparison table;
• the indicators rejected, combined, or revised and the reasons for those decisions;
• the final verified portfolio.

Use conversation export or another clear record available in the approved platform. The purpose is to make the analytical process visible, not to reward the longest transcript.

Responsible-Use Guardrails

Connection to Assignment 7

Assignment 7: AI-Assisted Ethical Selection and Comparison of Sustainability Indicators

The assignment asks you to use two or three AI models, compare their candidate indicators and matrix-based analyses, and produce a justified portfolio of six to eight sustainability indicators for your final project. The complete submission requirements and evaluation criteria are provided in Canvas.

Main Point

Generative AI can widen the search for plausible indicators and make the initial research process more manageable. The ethics matrices provide the criteria needed to examine evidence, broader impacts, justice, boundaries, proxies, assumptions, and omissions. Comparison across models makes variability visible. Verification and final selection remain human responsibilities.