Freyja Labs

Professional Development for Houston ISD

Customized. Hands-on. Built for your teachers and your students.

Houston, TX · 184,109 students

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What we've been reading

From outside, what we notice

Houston ISD is the largest district in Texas and currently state-managed, with the Bluebonnet Learning curriculum initiative shaping the instructional landscape. The TEA's CS teacher-shortage designation tells the rest of the story: demand for computing instruction is outpacing the supply of dedicated specialists, and the sustainable path is helping existing teachers across content areas build computational thinking pedagogy they can use in their own classrooms. We co-design that work with your teachers — custom-built for Houston students, anchored in the lived experience your families bring to questions about weather, water, and trust.

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Next ↓ 02 · Sample lessons

Sample lessons — artifacts, not deliverables

We don't deliver lesson plans.

We deliver change for your teachers. The lessons below are the receipts.

These two examples were built for Houston ISD — but what we'll actually do together depends on what you tell us about where you are and where you want to go. We custom-build with your teachers around what your students, your community, and your district leadership are actually navigating: AI integration, CS/STEM integration, TX policy, the local context only your educators can read. The artifacts you'll see below are one shape that capability can take.

Tech lesson / with devices
micro:bit + AI · Weather data and forecast verification

Weather, Data, and Trust: When Should We Believe AI?

6–8 · Science · 90 minutes

Kit: micro:bit + sample lesson plan — "Weather, Data, and Trust: When Should We Believe What AI Tells Us?" (grades 6-8, science). Students collect weather data with micro:bit sensors, compare with AI-generated forecasts, and evaluate accuracy. Embedded AI literacy: verification protocol applied to AI weather predictions — when to trust, when to verify, when to override. Tailored to Houston's relationship with weather (hurricanes, flooding) — data literacy is personal here.

What's new — what wouldn't have happened before this PD

Without the PD, an AI weather forecast lands as the forecast. After: students measure conditions themselves and learn that local data is the ground truth — the move that matters most when the forecast involves a Houston bayou.

Show full lesson plan objectives · procedure · materials · assessment · teacher pack ▾

Content Objectives

  • Collect weather data using digital sensors at multiple microclimate sites
  • Compare measured weather data with predicted weather data
  • Identify the role of microclimate variation in forecast accuracy

AI Literacy Objectives

  • Compare AI weather predictions against student-measured ground truth
  • Apply structured verification practice to AI weather output
  • Articulate Houston-specific criteria for when to trust, verify, or override AI weather predictions

What Students Do

Phase 1 · 25 min Collect

Teams of 3+ deploy micro:bit weather sensors at multiple campus microclimates (open field, breezeway, parking lot, courtyard). Record at 5-minute intervals; note sky conditions.

Facilitation focus

Don't standardize sensor placement across teams. Different microclimates make Phase 2 richer. Move between teams every 5 minutes; check that students are recording observations *and* numerical readings. The qualitative notes are the wedge they'll use to challenge AI in Phase 3.

Watch for

Teams logging only numbers. Push them to write at least one observation per reading ("breeze picked up", "cloud passed over"). If the campus has visibly varied environments — shade vs. sun, paved vs. planted — push teams to spread out.

Phase 2 · 30 min Compare

Teams query an AI weather tool for forecasts covering the same time and location windows. Document where AI predictions match measurements and where they diverge — especially after a Houston morning rain.

Facilitation focus

Frame the AI tool as a teammate, not an authority. When the AI prediction is wrong, students often default to "we'll fix our data." Interrupt that — the goal is to surface where AI and ground-truth diverge, not to reconcile.

Watch for

Teams that find zero divergence. Either they're smoothing data unconsciously, or the AI is generic enough to match anything. Have them pick a single 5-minute window and compare in extreme detail.

Phase 3 · 35 min Evaluate

Apply structured verification practice to the AI forecast. Class develops a Houston-specific trust framework: when is AI worth trusting on weather, and when does local microclimate data win?

Facilitation focus

The class trust guidelines are the deliverable. Push for specificity: not "AI is bad at humidity" but "AI underestimates humidity in conditions like ours when [specific local condition]." Local knowledge + data = the trust criteria.

Watch for

Generic statements ("AI is sometimes wrong"). Reject these gently — every guideline must reference a specific divergence the team observed.

A four-step verification protocol your teachers will build with us

A practice students learn once and apply to any AI output, in any subject, for the rest of their lives.

1. Check the source

Where did the AI get its data? Is it the same data we used or generated?

2. Check the reasoning

How did the AI reach its conclusion? Can we follow the logic?

3. Check against reality

Does the output match what we observed with our own senses, instruments, or knowledge?

4. Check yourself

What might we have missed? What would we want a second opinion on?

More on the thinking behind this — the framework we built it from.

Materials

  • micro:bit with temperature, humidity, and barometric sensors (included in kit)
  • USB cables and student devices
  • Access to a public AI weather forecast tool
  • Houston-area microclimate site map
  • Data recording sheet and chart paper

Assessment

Each team produces a one-page artifact: their findings, the AI output they evaluated, and a written verdict on when this kind of AI work is worth trusting.

Trust framework names at least one specific divergence from a Houston microclimate and a defensible criterion derived from it.

Teacher pack — everything you need to teach this

For the Facilitator

Prior Knowledge Required
  • Read and create simple data tables and bar/line graphs
  • Distinguish between an observation (what we measured) and an inference (what we conclude)
  • Familiarity with one-step variable assignment in block-based or text-based code
Exit Ticket

"Describe one moment today when your direct measurement told you something the AI missed. What did you measure, and what should the AI have done differently?"

Look for
  • Specific reference to a measurement (number + unit + location)
  • Specific reference to what the AI output said
  • A concrete claim about what the AI should have changed (input, comparison, caveat)
Anticipated Misconceptions

"If the AI says it, it must be right — it has access to all the data."

→ Show the AI a deliberately wrong dataset and have students predict the (wrong) output. Reinforce: AI confidence ≠ AI correctness. The AI processes whatever input it receives, including noise and bias.

"Our sensor data is wrong because it doesn't match the AI."

→ Have students re-measure with a second device or different location. Direct measurement is the ground truth — divergence with AI is a signal worth investigating, not an error to "fix."

"The AI is broken if it gives a different answer to the same question twice."

→ This is a feature, not a bug. Use it to discuss probabilistic vs. deterministic systems. Two valid outputs can describe the same data — students should learn to ask "what stayed the same?"

Differentiation
Differentiation 1 / 4
Below grade

Pre-load the data table with column headers and one example row. Pair with a peer for the AI comparison phase. Provide a sentence frame for the evaluate phase: "We trust the AI when ___ because ___. We don't trust it when ___ because ___."

At grade

Standard procedure as written.

Above grade

Add a fourth phase: students design a follow-up experiment that would resolve a specific AI-vs-observation disagreement they found. They write the protocol; they don't need to execute it in class.

Multilingual learners

Provide vocabulary cards in advance: sensor, prediction, observation, evidence, divergence. Allow team discussion in students' home language; final class artifacts can be authored bilingually. The verification protocol works in any language — emphasize that direct measurement and lived knowledge are the most authoritative inputs.

Slide Cues — 6 slides
Slide cues 1 / 6
Slide 1 What we are doing today
  • Collect real data on our campus
  • Compare it with what an AI predicts
  • Decide together when AI is worth trusting
Slide 2 The verification protocol
  • Source · Reasoning · Reality · Yourself
  • You don't need to know how the AI works to check its work
  • A protocol, not a checklist — adapt to the situation
Slide 3 Phase 1 — Collect
  • Teams of 3 deploy sensors at chosen spots
  • Record at intervals; vary your locations
  • Log a written observation with each reading
Slide 4 Phase 2 — Compare
  • Input your data into the AI tool
  • Find at least 3 places it agrees, 3 places it diverges
  • Don't correct your data to match the AI
Slide 5 Phase 3 — Evaluate
  • Apply structured verification practice to your divergences
  • Present one trust guideline based on what you found
  • Class builds the shared trust framework together
Slide 6 Exit ticket
  • One moment when your measurement beat the AI
  • Be specific — number, location, what AI missed
Standards Alignment — 13 frameworks
Standards 1 / 13
NGSS MS-ESS3-3

Apply scientific principles to design monitoring method for human impact

NGSS MS-ETS1-3

Analyze data from tests to determine best design solutions

CCSS Math 6.SP

Statistics & Probability — Grade 6

CCSS Math MP.4

Model with mathematics — Math Practice

CSTA 2-DA-08

Collect data using computational tools; transform for reliability

CSTA 2-DA-09

Refine computational models based on generated data

CSTA 2-IC-21

Discuss bias and accessibility in technology design

ISTE ISTE-3b

Evaluate accuracy, credibility, relevance of information

ISTE ISTE-5b

Collect data and identify data sets; use tools to analyze data

TX TEKS Technology Applications TA.6.2.A

Collect, transform, and analyze data using digital tools and explain how the data inform a decision

TX TEKS Technology Applications TA.7.2.A

Use computational tools to collect, transform, analyze, and visualize data to answer a question

TX TEKS Technology Applications TA.8.2.A

Use computational models, including simulations, to investigate phenomena and explain results

TX TEKS ELA ELA.6.6.C

Use text evidence to support an appropriate response

Family / Guardian Letter — copy & paste, edit to fit

Dear families, This week your student is learning a skill that will matter for the rest of their lives: how to decide when to trust an AI system. In this lesson, students used real sensors to measure conditions around our school and compared what they measured with what an AI predicted. The point is not that AI is bad — the point is that AI works best when paired with someone who knows the real situation. Your student is learning to be that someone. We call the protocol the verification protocol. It has four steps: check the source the AI used, check the reasoning, check the result against reality, and check yourself for what you might have missed. You can use this with your student at home — every time an AI assistant gives you an answer, ask: "How would we check this?" Questions? hello@freyjalabs.com — Freyja Labs (working with Houston ISD)

↑ Top of Tech lesson ↑ Top of Lessons Next: Unplugged Lesson →
Unplugged lesson / no screens
No screens · Hurricane forecast critique

The Hurricane Forecast Challenge: Who Do You Trust With Your Family's Safety?

6–8 · Science / Social Studies · 60 minutes

The Hurricane Forecast Challenge: Who Do You Trust With Your Family's Safety? — Teams examine printed historical hurricane forecast data (predicted vs. actual paths). They apply the verification protocol to determine when AI forecasts were reliable and when local knowledge — the bayous, the drainage patterns, the flat terrain — would have produced better decisions. Every Houston student who has watched a bayou rise has data no training set contains.

What's new — what wouldn't have happened before this PD

Without the PD, examining historical hurricane forecasts is a data exercise. After: students apply a structured verification protocol to forecasts that affected real Houston families and learn that lived experience of a bayou rising is data no training set contains.

Show full lesson plan objectives · procedure · materials · assessment · teacher pack ▾

Content Objectives

  • Read and interpret hurricane forecast track data
  • Distinguish between model error and data error
  • Construct an evidence-based recommendation under uncertainty

AI Literacy Objectives

  • Identify what AI forecasts capture and what they miss
  • Distinguish between forecast confidence and forecast correctness
  • Articulate when local knowledge should override an AI forecast

What Students Do — No Screens, No Devices

Phase 1 · 20 min Examine

Teams of 3+ receive printed packets of historical hurricane forecasts (predicted track vs. actual). Note where forecasts agreed with reality and where they diverged.

Facilitation focus

Print the artifact packets in color so detail is preserved. Don't tell students which AI claims are "right" — let them notice divergence on their own. Their lived knowledge of the topic IS the comparison standard. Treat it that way explicitly.

Watch for

Teams that pick a "winning" artifact immediately. Slow them down — every artifact reflects the AI's best guess given its inputs. The question is not which is right but how anyone could have known in advance.

Phase 2 · 15 min Investigate

Using the methodology summary cards, teams identify whether each divergence was a model failure or a missing-data failure. Document local knowledge — bayou patterns, drainage, terrain — that would have improved the forecast.

Facilitation focus

Distinguish three error types: factual (X is asserted but isn't true), framing (the description emphasizes one thing while ignoring others), absence (something important is left out entirely). Most AI artifacts fail in framing and absence, not facts.

Watch for

Teams that only catch factual errors. Push deeper — what story is the AI telling? Whose perspective is implicit? What did it not have access to?

Phase 3 · 25 min Argue

Each team responds to a family-decision prompt: a relative in Galveston, 36 hours from landfall. Apply structured verification practice to recommend evacuate/stay and defend with evidence.

Facilitation focus

Frame the argument as advice to a real decision-maker who will act on it. Students must commit to a recommendation AND name specifically what would change their mind.

Watch for

Hedging ("we can't really know"). True — but the decision still has to be made. Push students to commit to a recommendation AND explain what new information would flip it.

Materials

  • Printed historical hurricane forecast packets (Harvey 2017 and Ike 2008 — predicted vs. actual paths) at landing page
  • Houston-area watershed and bayou map
  • Forecast methodology summary cards
  • Verification protocol reference card; chart paper, markers

Assessment

Each team produces a one-page artifact: their findings, the AI output they evaluated, and a written verdict on when this kind of AI work is worth trusting.

Recommendation cites at least three pieces of evidence and names what new information would change the decision.

Teacher pack — everything you need to teach this

For the Facilitator

Prior Knowledge Required
  • Read and discuss informational text in small groups
  • Cite evidence to support a claim — written or verbal
  • Familiarity with the difference between a prediction and a confirmed result
Exit Ticket

"An AI tool gives someone you care about a recommendation. What three things should they check before they accept it?"

Look for
  • At least one item references the source or input data the AI used
  • At least one item references the AI's reasoning or comparison with known facts
  • At least one item references checking with a person, lived experience, or independent source
Anticipated Misconceptions

"AI is just like a calculator — if you give it the right numbers, you get the right answer."

→ Use a worked example where two students give the same prompt and get different outputs. AI is more like a human reader making a judgment call than a calculator computing a formula.

"If we can't see the math, we just have to trust it."

→ Pivot the protocol — "Check the reasoning" — to focus on what we CAN check: source, comparison to known facts, internal consistency. You don't need the math to evaluate a claim.

"AI hallucinations only happen with chatbots."

→ Show a printed AI example that contains a confident but factually wrong statement. Hallucinations are a property of how generative models work, not a chatbot quirk.

Differentiation
Differentiation 1 / 4
Below grade

Reduce to two artifacts in the packet (instead of three or four). Provide a graphic organizer with three columns: What the AI said / What I know / What I would tell someone who asked me. Sentence frames provided for the final argument.

At grade

Standard procedure as written.

Above grade

Add a meta-question: students draft two or three rules for when the kind of AI tool in this lesson should and should not be used by adults in their lives. The rules are evidence-cited from the packet.

Multilingual learners

Encourage discussion in students' home language. The artifacts in the packet can be discussed in any language — the analysis transfers. Final class artifacts can be authored bilingually. Pair vocabulary cards if helpful: source, reasoning, reality, evidence.

Slide Cues — 6 slides
Slide cues 1 / 6
Slide 1 Real decisions, real evidence
  • AI tools are showing up in decisions that matter
  • Your job today: evaluate whether the AI's reasoning holds up
  • No screens. Just the artifacts in front of you.
Slide 2 Verification protocol — without the machine
  • Source · Reasoning · Reality · Yourself
  • Same protocol whether you're looking at a sensor reading or a printed AI claim
  • Adapt to what's in front of you
Slide 3 Phase 1 — Examine
  • Read the artifacts carefully
  • Note what the AI got right and wrong
  • Don't pick a winner yet
Slide 4 Phase 2 — Investigate
  • WHY did the AI reach this conclusion?
  • What data did it have? What did it miss?
  • Was it the model, or what the model knew?
Slide 5 Phase 3 — Argue
  • Make a defensible recommendation
  • "It depends" is allowed if followed by "specifically, on these things"
  • Cite evidence from the packet
Slide 6 Exit ticket
  • Three things to check before accepting any AI recommendation
Standards Alignment — 9 frameworks
Standards 1 / 9
CCSS ELA RI.7

Reading Informational Text — Grade 7

CSTA 2-IC-21

Discuss bias and accessibility in technology design

CSTA 2-IC-23

Describe tradeoffs between public and private information

ISTE ISTE-3b

Evaluate accuracy, credibility, relevance of information

ISTE ISTE-7b

Use collaborative tech to examine issues from multiple views

NCSS C3 D2.Civ.14.6-8

Compare historical/contemporary means of changing society

TX TEKS Technology Applications TA.8.3.A

Discuss the social, economic, and ethical implications of computing innovations and emerging technologies, including issues of bias

TX TEKS ELA ELA.6.6.C

Use text evidence to support an appropriate response

TX TEKS ELA ELA.6.10.C

Compose argumentative texts, including opinion essays, using genre characteristics and craft

Family / Guardian Letter — copy & paste, edit to fit

Dear families, This week your student practiced something most adults haven't been formally taught: how to evaluate an AI-generated claim before accepting it. In this lesson, students worked from printed artifacts — no screens — and applied a four-part verification protocol: check the source, check the reasoning, check the result against reality, and check yourself. They learned that the right answer to "should I trust this AI?" is almost always "let me check first." At home, you can use the same protocol. The next time an AI assistant gives your family information, ask your student: "What would we need to check before we acted on this?" Questions? hello@freyjalabs.com — Freyja Labs (working with Houston ISD)

↑ Top of Unplugged lesson ↑ Top of Lessons Next: How we'd work together →

Worth saying again: the lessons above are receipts, not the goal. The point of the engagement is change for your teachers — their confidence to design the next ten lessons themselves, for whatever Houston ISD faces next. We don't deliver lesson plans. We deliver capability.

More on how we think about this work

Next ↓ 03 · How we'd work together

Engagement Options

How We Can Work Together

We don't sell a packaged curriculum — every engagement is shaped around what your district tells us it needs. The options below are starting shapes; the actual work gets co-designed with your team. Click any that look promising and tell us what you're thinking.

Click any option below to mark it as interesting — then use the form to send a quick note.

Next ↓ 04 · Reach out

We do not provide generic materials. We provide the empowerment and support for teachers to build lessons like these — tailored to their students, grounded in their community's experience.

Mike Borowczak, Ph.D.

Andrea C. Burrows Borowczak, Ed.D.

Where growth begins.

hello@freyjalabs.com