Salt Shaker Press: Notebook Analysis

Here is the complete transcript for the YouTube video titled “A.I.💥 How To Analyze Your Documents with Google's NotebookLM! Quick! Easy! Game Changer! AI Tutorial” (Oct 17, 2025, 18:19):

Full Transcript

Watching this video, you will become an expert at using artificial intelligence AI to summarize and analyze massive volumes of textual information with 100% accuracy in minutes using Google’s Notebook LM. I will show you how step by step. I'll make it easy — this will be a game changer for you.

First, let me introduce you to Notebook LM. Notebook LM is powered by Gemini, Google’s artificial intelligence large language model similar to ChatGPT. Notebook LM gives us two major advantages compared to other AI tools for text analysis: it can handle large volumes of text and provide very high accuracy when properly instructed.

Notebook LM is organized into notebooks. Each notebook can handle up to 50 sources, and each source can contain up to 500,000 words — for comparison, the largest Harry Potter book had 257,000 words. So one source can hold about two Harry Potter books.

Another advantage is Notebook LM’s ability to overcome AI hallucinations — instances where AI provides false or made-up answers. MIT, Google, and OpenAI have documented this phenomenon. To minimize hallucinations, follow three steps:

Isolate the AI (use only your sources, not the internet),
Ground it (upload your documents so it only references them),
Instruct it properly with precise prompts.

Notebook LM achieves isolation by only looking at your uploaded documents, grounding by analyzing what you provide, and proper instruction through clear, accurate prompts.

Now let’s begin the step-by-step tutorial.
Go to notebooklm.google.com and you’ll see your gallery of notebooks. Click Create New Notebook. This will open a blank notebook. As a demonstration, I created one using the book Designing Your Life by Bill Burnett and Dave Evans, professors at Stanford — about 34,000 words in total.

I uploaded both the table of contents and the full text. To upload files, click Add, then drag and drop your PDF or text file (Word docs aren’t supported). You can also paste text directly.

After uploading, use the checkboxes to select which sources the AI will read. In the middle of your screen is the chat box — this is where you type your prompts.

For example, type:

“Give me a 200-word summary using only direct quotes and put quotes in quotation marks.”

Notebook LM will return a strictly quoted summary. This is my method to ensure 100% accuracy — it prevents hallucination, speculation, or interpretation.

Here’s my general template prompt:

“Use only direct quotes in your response and put quotes in quotation marks.”

Then specify what to generate — such as an outline, summary, or mind map. For example:

“The attached file ‘Table of Contents’ has 13 chapters. Generate an outline matching these headings, about 2,000 words. Use only direct quotes in your response.”

I often generate:

An outline,
A mind map,
A summary, and
A Top 20 list of takeaways.

These different outputs reveal different types of insights.

When creating an outline, you may experiment with length (e.g., 500, 1,000, or 2,000 words) to match your depth needs. You will notice all quotations are marked — Notebook LM shows where each quote comes from (e.g., Chapter 5 of Designing Your Life).

In the prompt box, you can also try commands like:

“Summarize in 100 words using direct quotes only.”
“List the top 10 takeaways.”

Notebook LM can automatically generate audio overviews (like podcasts), video slideshows (like PowerPoint), mind maps, reports, flashcards, and quizzes. All these features are on the right under Studio.

A mind map visually connects ideas as nodes. You can also prompt it textually using the word “nodes.” While export is static for now, it’s very effective for visually understanding relationships.

The Reports tab allows you to generate:

Briefing Docs
Study Guides
Blog Posts
Strategic Memos
Personal Development Plans
Explainers
Narrative Summaries

Out of these, the Briefing Doc is the most useful for quickly getting an executive summary of long texts. Simply upload your text and click Reports → Briefing Doc to get structured summaries in under a minute.

A key workflow tip: Notebook LM does not save chat history after you close the browser.
So if you like an output, click Save to Notes or copy and paste it into Word before refreshing or closing. Long chats may sometimes confuse Gemini, so if you get inconsistent results, click Refresh to reset.

Summary Recommendation:
For high-accuracy document analysis with Notebook LM:

Log in with your Google account.
Upload your text (PDF or pasted text).
Click Reports → Briefing Doc.

Within a minute, you’ll have clear, structured insights ready for review and export.

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Here are 50 text analysis prompt templates, categorized by the type of analysis. You can replace the bracketed [...] sections with your specific text or parameters.

1. Summarization and Core Idea

Summarize the following text in [number] sentences: [Insert Text]
Provide a one-paragraph summary of the key points in: [Insert Text]
What is the main argument or thesis of this passage? [Insert Text]
Extract the [3] most important takeaways from this article: [Insert Text]
Generate a concise title for the following text that captures its core theme: [Insert Text]

2. Sentiment and Tone Analysis

Analyze the overall sentiment (e.g., positive, negative, neutral) of the following text: [Insert Text]
What is the author's tone in this passage? (e.g., sarcastic, optimistic, critical, formal): [Insert Text]
Identify the primary emotions (e.g., joy, anger, fear) expressed in this text: [Insert Text]
Extract all phrases from the text that indicate a [positive/negative/neutral] sentiment: [Insert Text]
How does the sentiment of the text change from the beginning to the end? [Insert Text]

3. Keyword and Topic Extraction

List the top [5] most important keywords or keyphrases in this text: [Insert Text]
What are the main topics or themes discussed in the following document? [Insert Text]
Identify any recurring concepts or ideas in this text: [Insert Text]
What is the central subject of this text? [Insert Text]
Generate a list of hashtags relevant to this text: [Insert Text]

4. Text Classification and Categorization

Classify this text into one of the following categories: [Category A], [Category B], or [Category C]. Text: [Insert Text]
What is the intent of the following customer query? (e.g., complaint, question, compliment, purchase): [Insert Query]
Is this email [Spam] or [Not Spam]? Email: [Insert Email Text]
What is the genre of this text? (e.g., news report, academic paper, marketing copy, fiction): [Insert Text]
Does this text primarily express an [Opinion] or state a [Fact]? [Insert Text]

5. Information and Entity Extraction

Extract all personal names mentioned in this text: [Insert Text]
List all organizations and geographic locations found in the following passage: [Insert Text]
Extract all dates, times, and monetary values from this text: [Insert Text]
Identify all instances of [specific entity, e.g., product names, legal terms] in this document: [Insert Text]
Create a JSON object containing all extracted entities (Person, Organization, Location) from this text: [Insert Text]

6. Language, Style, and Readability

Analyze the writing style of this text. Is it formal or informal? Simple or complex? [Insert Text]
What is the readability level of this passage? (e.g., 8th grade, college graduate): [Insert Text]
Identify any figurative language (e.g., metaphors, similes, personification) used in this text: [Insert Text]
What is the author's point of view? (e.g., first-person, third-person objective, third-person omniscient): [Insert Text]
Analyze the sentence structure of this text (e.g., are sentences predominantly long or short? Simple or complex?): [Insert Text]

7. Argument and Persuasion Analysis

What is the primary claim the author is making? [Insert Text]
What evidence or supporting points does the author use to back up their claim? [Insert Text]
Identify any logical fallacies or weaknesses in this argument: [Insert Text]
What persuasive techniques are used in this text? (e.g., appeal to emotion, appeal to authority, logos): [Insert Text]
Who is the target audience for this text, and how does the language cater to them? [Insert Text]

8. Comparative Analysis

Compare and contrast the main arguments of the following two texts: [Text A] and [Text B]
How do the tones of these two passages differ? [Text A] and [Text B]
Identify the common themes and unique points in these [number] texts: [Insert Texts]
Which of these texts is more [persuasive/formal/biased], and why? [Text A] and [Text B]
Analyze the differences in writing style between [Author A's text] and [Author B's text].

9. Contextual Analysis and Q&A

Based on the text, answer the following question: [Insert Question]. Text: [Insert Text]
What are the underlying assumptions made by the author in this text? [Insert Text]
What is the broader context or implication of this passage? [Insert Text]
Find the sentence in the text that best supports this statement: [Insert Statement]. Text: [Insert Text]
Detect any potential bias (e.g., political, gender, cultural) in this text and explain your reasoning: [Insert Text]

10. Text Transformation and Generation (Based on Analysis)

Rewrite this text in a more [formal/informal/concise/persuasive] style: [Insert Text]
Translate this text from [Language A] to [Language B]: [Insert Text]
Identify the relationship between [Entity 1] and [Entity 2] in this text: [Insert Text]
Generate [3] discussion questions based on the key themes of this text: [Insert Text]
Simplify this complex text for a general audience: [Insert Complex Text]

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Here is a list of 50 data analysis templates, categorized by their purpose, from project planning and data cleaning to specific models, statistical tests, queries, and final dashboards.

Project & Process Templates

These templates provide a structure for the entire data analysis workflow, from planning to execution.

Data Analysis Project Plan: A document (e.g., in Word or a project management tool) outlining project goals, scope, key questions, stakeholders, data sources, methodology, and timeline.
Data Cleaning Checklist (Spreadsheet): A step-by-step checklist to ensure data quality, covering tasks like removing duplicates, handling missing values, standardizing formats, and correcting errors.
Exploratory Data Analysis (EDA) Notebook (Jupyter/RMarkdown): A code template that provides a structured flow for initial data investigation: loading data, summarizing statistics, visualizing distributions, and identifying correlations.
Python Data Cleaning Script: A .py or Jupyter Notebook template with common pandas and numpy functions for cleaning a dataset (e.g., df.isnull().sum(), df.dropna(), df.fillna(), df.astype()).
R Data Cleaning Script (RMarkdown): An .Rmd template using tidyverse packages (like dplyr and tidyr) for common cleaning tasks (e.g., is.na(), drop_na(), mutate_at(), separate()).
A/B Test Results Template: A spreadsheet or report structured to log the hypothesis, test duration, sample size, and key metrics (e.g., conversion rate) for both control and variant, along with statistical significance (p-value).

Strategic & Business Analysis Frameworks

These are high-level frameworks used to understand a business's position and environment.

SWOT Analysis: A 2x2 grid template to identify Strengths, Weaknesses, Opportunities, and Threats.
PESTLE Analysis: A structured document to analyze external macro-environmental factors: Political, Economic, Social, Technological, Legal, and Environmental.
Porter's Five Forces Analysis: A diagram template to analyze an industry's competitiveness by examining five forces: Threat of New Entrants, Bargaining Power of Buyers, Bargaining Power of Suppliers, Threat of Substitutes, and Competitive Rivalry.
Root Cause Analysis (5 Whys): A simple document template that guides the user to ask "Why?" repeatedly (typically five times) to trace a problem back to its origin.
Cost-Benefit Analysis: A spreadsheet template that lists all projected costs and benefits of a project, calculates metrics like Net Present Value (NPV) and Return on Investment (ROI), and aids in decision-making.

Specific Analytical Models

These are templates for common, reusable analytical models used in marketing, sales, and finance.

Cohort Analysis (Retention Matrix): A spreadsheet template where rows represent a user acquisition cohort (e.g., "January 2025 Users") and columns represent time periods (Month 0, Month 1, etc.), with cells showing the percentage of the cohort that remained active.
RFM Analysis (Recency, Frequency, Monetary): A spreadsheet template that segments customers by ranking them on three factors: how recently they purchased, how frequently they purchase, and the monetary value of their purchases.
Customer Lifetime Value (CLV) Calculator: A spreadsheet model that inputs variables like Average Purchase Value, Purchase Frequency, and Customer Lifespan to calculate the total revenue a business can expect from a typical customer.
Customer Churn Analysis: A template (often in a BI tool or spreadsheet) that tracks churn rate over time and segments it by customer demographics, acquisition channel, or product plan to identify drivers.
Sales Funnel Analysis: A spreadsheet or dashboard template that visualizes the conversion rates at each stage of the sales process (e.g., Lead -> MQL -> SQL -> Closed-Won), highlighting bottlenecks.
Market Basket Analysis: A template for organizing transaction data to find "if-then" association rules (e.g., "If a customer buys diapers, they are 80% likely to also buy beer").
Customer Segmentation Analysis: A report or notebook template for grouping customers into distinct segments based on shared characteristics (e.g., demographic, behavioral, psychographic).

Statistical Analysis Templates (Excel/Google Sheets)

These templates are set up to perform specific statistical tests by simply inputting your data.

Descriptive Statistics Summary: A spreadsheet that automatically calculates Mean, Median, Mode, Standard Deviation, Variance, Range, Min, and Max for a given dataset.
Regression Analysis Template: An Excel sheet using the Data Analysis ToolPak (or LINEST function) to model the relationship between a dependent variable and one or more independent variables, providing R-squared, coefficients, and p-values.
T-Test Template (Two-Sample): A spreadsheet set up to compare the means of two independent groups, calculating the t-statistic and p-value to determine if the difference is statistically significant.
ANOVA Template (One-Way): A spreadsheet using the Data Analysis ToolPak to compare the means of three or more groups, providing the F-statistic and p-value.
Chi-Square Test Template: A spreadsheet set up with a contingency table (observed frequencies) that calculates expected frequencies and the Chi-Square statistic to test for independence between two categorical variables.
Pearson Correlation Matrix: An Excel sheet that uses the CORREL function or Data Analysis ToolPak to create a matrix showing the correlation coefficient (r) between all pairs of variables in a dataset.

SQL Query Templates

These are reusable SQL code snippets for pulling common business metrics from a database.

Daily Active Users (DAU) / Monthly Active Users (MAU): A query to get a COUNT(DISTINCT user_id) from an event log, grouped by day or month.
User Retention (Cohort Query): A complex query that uses a Common Table Expression (CTE) to find a user's "first seen" date and then tracks their activity in subsequent periods.
Monthly Recurring Revenue (MRR): A query that sums the monthly_fee from a subscriptions table, often grouped by month to show trends (SUM(mrr) GROUP BY DATE_TRUNC('month', date)).
Customer Churn Rate: A set of queries that first count active customers at the start and end of a period, then calculate the percentage who did not return.
Top N Products by Sales: A query that joins order_items and products tables, then uses SUM(price) and GROUP BY product_name, and finally ORDER BY total_sales DESC LIMIT 10.
Sales by Region: A query that joins sales, customers, and addresses tables to SUM(sales_amount) and GROUP BY state or country.
New vs. Returning Customers: A query that uses a CTE with a MIN(order_date) window function to flag a customer's first-ever order, then counts new vs. returning customers in a given period.

Dashboard & Report Templates

These are visual layouts (typically in tools like Power BI, Tableau, or Looker Studio) for presenting key performance indicators (KPIs).

Marketing

Website Traffic Dashboard (GA4): Tracks key metrics like Users, New Users, Sessions, Engagement Rate, and Top Traffic Sources (Organic, Direct, Referral).
PPC Campaign Performance Dashboard: Visualizes ad performance, tracking Impressions, Clicks, Click-Through Rate (CTR), Cost Per Click (CPC), Conversions, and Return on Ad Spend (ROAS) by campaign.
Email Marketing KPI Dashboard: Tracks campaign metrics like Open Rate, Click-Through Rate (CTR), Unsubscribe Rate, and Conversions per email.
Social Media Engagement Report: A dashboard showing Follower Growth, Likes, Shares, Comments, and Engagement Rate by platform (e.g., Instagram, TikTok, LinkedIn).
SEO Performance Dashboard: Tracks Organic Traffic, Keyword Rankings, Backlinks, and Organic Conversion Rate.

Sales

Sales Pipeline Dashboard: A funnel or bar chart visualizing the number of deals and their total value at each stage (e.g., Prospecting, Qualification, Proposal, Closed).
Sales Rep Performance Dashboard: A leaderboard tracking KPIs per rep, such as Quota Attainment, Deals Closed, Activities Logged, and Average Deal Size.
E-commerce Sales Dashboard: Focuses on e-commerce metrics like Total Revenue, Average Order Value (AOV), Conversion Rate, and Top-Selling Products.

Finance

Financial KPI Dashboard (Executive View): A high-level view of core metrics like Revenue, Gross Profit Margin, Operating Income (EBITDA), and Net Profit Margin over time.
Cash Flow Analysis Dashboard: Visualizes Operating Cash Flow, Investing Cash Flow, and Financing Cash Flow, along with the ending cash balance.
Budget vs. Actual Analysis: A report template (often Excel) that compares planned (budgeted) expenses and revenue against actual results, showing the variance.

HR & Operations

HR KPI Dashboard: Tracks key workforce metrics like Headcount, Employee Turnover Rate, Time to Hire, and Absenteeism Rate.
Employee Performance Dashboard: A management template showing performance review scores, goal completion rates, and productivity metrics by department or team.
Employee Diversity & Inclusion Dashboard: Visualizes workforce composition by gender, ethnicity, age, and pay equity across different roles and seniority levels.
Recruitment Funnel Dashboard: Tracks the applicant pipeline from Application Received -> Screening -> Interview -> Offer -> Hired, showing conversion rates at each stage.

Customer Support

Customer Service KPI Dashboard: Tracks operational metrics like Ticket Volume, First Response Time (FRT), Average Resolution Time, and Tickets Solved.
Customer Satisfaction (CSAT) Dashboard: Visualizes CSAT scores, Net Promoter Score (NPS), and Customer Effort Score (CES) over time, often segmented by support agent or product area.
Help Center / Self-Service Dashboard: Tracks metrics for a knowledge base, such as Top Searched Articles, Article Views, and "Was this article helpful?" scores.
SaaS Key Metrics Dashboard: A consolidated dashboard for subscription businesses, tracking MRR, Churn Rate, CLV, and Customer Acquisition Cost (CAC) all in one place.
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Here are 50 educational analysis prompt templates, categorized to help you analyze student data, curriculum, teaching methods, and broader educational programs.
🧑‍🎓 Student Performance & Growth
These prompts focus on analyzing individual and group data to understand student learning, identify needs, and track progress.
1. Identify Learning Gaps: "Analyze the [assessment name] data for [grade level/subject] to identify the top 3-5 concepts or skills where students struggled most."
2. Growth Over Time: "Compare student performance on the [pre-assessment] to the [post-assessment] for [unit name]. Which students showed the most growth, and which showed the least?"
3. Subgroup Achievement Gaps: "Disaggregate the [standardized test/benchmark] results by [student subgroup, e.g., ELL, IEP, socioeconomic status]. What achievement gaps exist, and how have these gaps changed since [last benchmark]?"
4. Identify At-Risk Students: "Based on [attendance data, formative assessment scores, and behavioral logs], generate a list of students who are at risk of failing [subject/course] and summarize the primary indicators for each."
5. High-Achiever Analysis: "Analyze the work samples and assessment data for students who consistently perform above grade level in [subject]. What common strengths, strategies, or learning patterns do they exhibit?"
6. Intervention Effectiveness: "Evaluate the progress monitoring data for students in [Tier 2/Tier 3 intervention program]. What percentage of students are on track to meet their goals? What adjustments are indicated?"
7. Formative vs. Summative: "Compare the class performance on [formative assessments/exit tickets] throughout the [unit name] with their final scores on the [summative test]. Where did formative data successfully predict summative performance, and where were there discrepancies?"
8. Attendance & Performance Correlation: "Analyze the correlation between student attendance rates and their final grades in [course/subject] for the [past semester/quarter]. What patterns emerge?"
9. Homework/Practice Impact: "Correlate the completion and accuracy rates of [homework/practice assignments] with student scores on [quiz/test] for [topic]. What is the apparent impact of practice on mastery?"
10. Student Self-Assessment Analysis: "Summarize student responses from the [self-assessment/reflection survey] for [project/unit]. What are the main themes in what students perceived as their strengths and weaknesses?"
📚 Curriculum & Assessment
These prompts help evaluate the effectiveness, alignment, and rigor of curriculum, lessons, and assessments.
1. Standard Alignment: "Review the [unit plan/lesson plans] for [subject/grade] and map all activities and assessments to their corresponding [state/Common Core] standards. Identify any gaps or misalignments."
2. Assessment Rigor (Bloom's/DOK): "Analyze the [assessment name] by classifying each question according to [Bloom's Taxonomy/Depth of Knowledge levels]. What percentage of the assessment targets higher-order thinking (Analyze, Evaluate, Create)?"
3. Item Analysis: "Perform an item analysis on the [multiple-choice test]. Which questions were most frequently missed? Which questions had a low correlation with overall high performance (i.e., high-scorers got them wrong)?"
4. Curriculum Pacing: "Compare the [curriculum pacing guide] to the actual topics covered by [date]. Is the current pacing on track? Which units required significantly more or less time than planned, and why?"
5. Resource Effectiveness: "Analyze teacher and student feedback on the [new curriculum material/textbook/software]. What are the most frequently cited strengths and weaknesses regarding engagement, accessibility, and standard alignment?"
6. Assessment Variety: "Audit the assessments used in [grade level/subject] over the past [semester]. What is the balance between formative and summative, and what variety of formats (e.g., project-based, selected-response, essay) is used?"
7. Rubric Analysis: "Analyze the scoring distribution for [project/essay] using [rubric name]. Which rubric criteria had the lowest average scores across the class? What does this imply about instruction?"
8. Vertical Alignment: "Compare the [end-of-unit assessment] for [6th grade math] with the [beginning-of-unit pre-assessment] for [7th grade math]. How well do the 6th-grade mastery expectations prepare students for 7th-grade content?"
9. Real-World Connection: "Evaluate the [unit name] for authentic, real-world connections. How many activities or assessments require students to apply skills in a context outside the classroom?"
10. Accessibility & Differentiation: "Review the [curriculum unit] and identify all built-in supports for [ELLs/students with IEPs]. Where are differentiation opportunities missing, and how could they be added?"
👩‍🏫 Teaching Methods & Instruction
These prompts are for reflecting on and analyzing the impact of specific teaching strategies and classroom practices.
1. Instructional Strategy Impact: "Compare student engagement and mastery data from the [unit using Strategy A, e.g., direct instruction] with data from the [unit using Strategy B, e.g., project-based learning]."
2. Questioning Patterns: "Analyze a [transcript/video] of a [class discussion].What is the ratio of open-ended vs. closed-ended questions? What is the average wait time given after a question?"
3. Student Engagement: "Based on [classroom observation data/walkthrough notes], what percentage of students were behaviorally, emotionally, and cognitively engaged during the [lesson part, e.g., small group work]?"
4. Feedback Quality: "Analyze a sample of [teacher feedback] provided on [student essays/projects]. Classify the feedback as [e.g., praise, corrective, or metacognitive]. How many comments provide a specific, actionable next step?"
5. Grouping Strategy: "Compare the outcomes and student engagement levels for [heterogeneous small groups] versus [homogeneous small groups] during the [reading/math] block."
6. Classroom Talk Ratio: "Analyze the [lesson recording] to determine the ratio of teacher-talk to student-talk. How does this ratio change during different phases of the lesson (e.g., warm-up, main activity, wrap-up)?"
7. Transition Efficiency: "Calculate the average time spent on transitions (e.g., from whole group to centers) based on [observation notes]. Where is instructional time being lost, and what procedures could be improved?"
8. Implementation Fidelity: "Evaluate the implementation of the [new reading program] based on [teacher logs and observation checklists]. To what extent are all components of the program being implemented with fidelity?"
9. Error Analysis: "Collect and categorize the most common errors students made on [assignment/quiz]. What misconceptions about the [topic] do these errors reveal, and how should instruction be adjusted?"
10. Student Voice Survey: "Summarize the key themes from the [student perception survey] regarding [teaching method, e.g., 'how helpful small group work is']. What do students identify as most helpful and least helpful?"
💻 Educational Technology & AI
These prompts focus on the integration, use, and impact of digital tools, software, and artificial intelligence in the learning process.
1. Tool Usage (Adoption): "Analyze the usage data for [software/platform name] over the past [month]. Which features are most used, and which are least used? What percentage of students/teachers have actively logged in?"
2. Tech Impact on Learning: "Correlate student usage of [adaptive math program] with their growth on [benchmark assessment]. Is there a demonstrable link between time-on-task and learning gains?"
3. AI Prompt Effectiveness: "Analyze the quality of student responses generated from [AI prompt A] versus [AI prompt B] for the [writing assignment]. Which prompt elicited more critical thinking and originality?"
4. Digital Workflow Efficiency: "Analyze the workflow for [assigning, completing, and grading digital work] using [LMS/platform]. Where are the bottlenecks or points of confusion for students or teachers?"
5. 1:1 Device Usage: "Observe and categorize how students are using their [laptops/tablets] during a typical [subject] lesson. What percentage of use is for [e.g., consumption, creation, communication, or off-task behavior]?"
6. AI Lesson Plan Analysis: "Evaluate the [AI-generated lesson plan] for [topic]. Assess its alignment with [learning standards], an-d its inclusion of [differentiation and engagement strategies]."
7. Student Collaboration on Tech: "Analyze the collaboration patterns within the [shared digital document/project]. Which students are contributing most, and what is the nature of their contributions (e.g., adding content, editing, commenting)?"
8. Digital Literacy Skills: "Assess student proficiency in [e.g., 'evaluating online sources'] based on their work in the [research project]. What specific digital literacy skills need to be explicitly taught?"
9. AI Feedback Utility: "Analyze student revisions after receiving feedback from [AI writing tool]. What types of AI suggestions (e.g., grammar, style, organization) did students most frequently accept, and did it improve the final product?"
10. ROI of EdTech Tool: "Analyze the costs of [EdTech tool] against its intended outcomes (e.g., improved reading scores, saved teacher time). Based on available data, what is the preliminary return on investment?"
🏛️ Program & School-Wide Analysis
These prompts take a high-level view, analyzing data for school improvement, program evaluation, and resource allocation.
1. School Improvement Plan (SIP) Goal Tracking: "Review the key performance indicators (KPIs) for our [SIP Goal 1, e.g., 'Improve 9th-grade math proficiency']. Based on [mid-year benchmark data], are we on track to meet this goal? What trends are emerging?"
2. Discipline Data Analysis: "Analyze [office referral and suspension data] for the [past semester], disaggregated by [student demographic, time of day, and location]. What are the most common infractions, and are there disproportionate patterns?"
3. Program Effectiveness: "Evaluate the effectiveness of the [e.g., new STEM lab, after-school tutoring program] by analyzing [participation rates, student feedback surveys, and related academic data]. Has the program met its stated objectives?"
4. Professional Development (PD) Impact: "Correlate [teacher participation in the 'Differentiated Instruction' PD] with [subsequent classroom observation data and student performance]. Is there evidence of strategy implementation and impact on learning?"
5. Chronic Absenteeism Trends: "Analyze chronic absenteeism rates ([% of students missing 10% or more of school]) by [grade level and student subgroup]. How do this year's trends compare to the previous [2 years]?"
6. Course-Taking Patterns: "Analyze high school course enrollment data. What percentage of students are enrolling in [AP/IB/dual-enrollment] courses? Are there demographic disparities in who accesses these advanced courses?"
7. New Teacher Retention: "Analyze retention data for teachers in their first [1-3 years]. How does our retention rate compare to the national average, and what are the primary reasons cited in [exit interviews]?"
8. Resource Allocation: "Analyze the [school budget] relative to [department/grade level] student performance. Are resources allocated equitably and in alignment with the school's primary improvement goals?"
9. Climate Survey Analysis: "Summarize the key findings from the [parent/staff/student climate survey]. What are the top 3 perceived strengths of the school and the top 3 areas for concern?"
10. Post-Secondary Readiness: "Analyze [graduation rates, college acceptance rates, and college persistence data] for the last [3 graduating classes]. How well are our graduates prepared for post-secondary success?"

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