Round 67
District AI Tutoring Rollout Strategy
A mid-sized school district has been piloting an AI-powered tutoring tool that supplements classroom instruction in math and reading. Pilot results show modest average score gains but uneven effects across student subgroups, some teacher pushback over reduced instructional autonomy, and rising costs for licenses and tech support. The district must decide how to proceed with a limited budget, a mandate to close achievement gaps, and pressure from parents both excited about personalized learning and worried about screen time and data privacy. The school board needs a clear policy before the next budget cycle, balancing equity, cost, teacher buy-in, and evidence quality.
Status
DECIDEDWinner
E · Adopt a hybrid model where AI tutoring supplements but does not replace any teacher-led small-group instruction, capping usage hours per week.Majority
E · Adopt a hybrid model where AI tutoring supplements but does not replace any teacher-led small-group instruction, capping usage hours per week.Duration
6264 msTopic
Options
Checks
Baselines
PPV
Top Influences
Samples
Answer Distribution
PPV
Influences
| Label | Role | Score |
|---|---|---|
| Gemini 3 Flash Preview | delegate | 1.415 |
| Claude Sonnet 5 | delegate | 1.247 |
| GPT 5.4 mini | delegate | 1.106 |
Voting matrix
Constructed Delegation Matrix
| To / From | Claude Sonnet 5 | GPT 5.4 mini | Gemini 3 Flash Preview |
|---|---|---|---|
| Claude Sonnet 5 | 0.000 | 0.247 | 0.000 |
| GPT 5.4 mini | 0.106 | 0.000 | 0.000 |
| Gemini 3 Flash Preview | 0.106 | 0.247 | 0.000 |
| A | 0.000 | 0.000 | 0.000 |
| B | 0.000 | 0.000 | 0.000 |
| C | 0.789 | 0.000 | 0.000 |
| D | 0.000 | 0.000 | 0.000 |
| E | 0.000 | 0.507 | 1.000 |
| F | 0.000 | 0.000 | 0.000 |
Embeddings
Sample Embeddings
Samples
Agent Responses
Given uneven subgroup effects, teacher pushback, and unresolved evidence quality, a full-scale rollout (B) or defunding (D) is premature, while decentralizing (F) risks equity gaps and inconsistent data privacy standards across schools. A one-year rigorous ran...
Given uneven subgroup effects, teacher pushback, and unresolved evidence quality, immediate district-wide scaling (B) or fully abandoning the tool (D) are premature; decentralized opt-in (F) risks inequitable access and inconsistent evidence gathering. A one-y...
Given uneven subgroup effects, teacher pushback, and cost/equity concerns, the district needs stronger evidence before scaling and should preserve teacher-led instruction rather than replacing it. A rigorous randomized evaluation (C) directly addresses "eviden...
Given uneven subgroup effects, teacher pushback, and unresolved cost/privacy concerns, immediate district-wide scaling (B) is premature and risky, while completely abandoning the tool (D) ignores its modest positive gains. A one-year pause for rigorous randomi...
Given uneven pilot effects, teacher pushback, and unresolved evidence quality, a district-wide rollout or full defunding would be premature; the responsible next step is to gather rigorous causal evidence before committing further budget. A one-year randomized...
Given uneven subgroup effects, teacher pushback, and unresolved cost/privacy concerns, immediately scaling district-wide (B) or fully deprioritizing the tool (D) both overreact to incomplete evidence. A one-size-fits-all mandate ignores the mandate to close ac...
Given uneven subgroup effects, teacher pushback, and unresolved data-privacy/cost concerns, immediate district-wide scaling (B) or fully abandoning the tool (D) both overreact to incomplete evidence. A rigorous year-long randomized evaluation (C) directly addr...
Given uneven subgroup effects, teacher pushback, and unresolved cost/privacy concerns, immediate district-wide scaling (B) or fully deprioritizing the tool (D) are premature given the modest but real gains observed. A rigorous evaluation (C) is prudent but del...
The pilot shows only modest gains and uneven subgroup effects, so a full district-wide rollout would be premature, especially with budget pressure and teacher concerns. The best immediate policy is to protect teacher-led instruction while using the AI as a lim...
The pilot shows some promise, but the effects are uneven and the costs, teacher concerns, and privacy worries are still unresolved. A full district-wide rollout is too risky, while a total pause or full rejection would ignore the evidence of modest gains; the ...
The pilot shows only modest gains and uneven subgroup effects, so expanding immediately district-wide would be risky given the limited budget and teacher concerns. A targeted expansion to the lowest-performing schools best aligns with the mandate to close achi...
The pilot shows only modest gains and uneven subgroup effects, so a full district-wide rollout is too risky given the budget and parent concerns. A pause for a rigorous randomized evaluation would best address the evidence gap before committing more funds, whi...
The pilot shows only modest benefits and uneven subgroup effects, so a full district-wide rollout is too risky given the budget and parent concerns. A targeted approach to the lowest-performing schools best aligns with the mandate to close achievement gaps whi...
The pilot shows only modest gains and uneven subgroup effects, so scaling district-wide would be premature, but waiting a full year for a new evaluation may not meet the budget and equity pressures. A targeted hybrid approach can preserve teacher-led instructi...
The pilot shows some promise, but the effects are uneven, the costs are rising, and teachers and parents have legitimate concerns about autonomy, screen time, and privacy. Since the district also has a mandate to close gaps and needs stronger evidence before l...
The district needs a decision that respects the evidence so far: modest gains, uneven subgroup impacts, and real concerns about cost, autonomy, and privacy. A hybrid rollout preserves teacher-led instruction while using AI as a supplement, which addresses buy-...
Option (E) addresses the core tensions by ensuring AI acts as a supplement rather than a replacement, thereby mitigating teacher pushback regarding instructional autonomy. By capping usage hours, it responds to parental concerns about screen time while maintai...
Option (E) addresses the core tensions by ensuring AI remains a supplemental tool, thus preserving teacher autonomy and addressing concerns about excessive screen time. This hybrid approach balances the personalized learning benefits for students with the need...
The district must address multiple conflicting factors, including uneven student outcomes, teacher concerns regarding autonomy, and parental anxiety over screen time. Option (E) provides a balanced approach by integrating the tool as a supplement rather than a...
A hybrid model (E) addresses the mandate to close gaps while mitigating teacher pushback by preserving instructional autonomy and preventing AI from replacing human-led groups. It directly tackles parental concerns regarding screen time through a usage cap and...
To select the optimal strategy, the district must balance the mandate to close achievement gaps and support personalized learning with concerns regarding costs, teacher autonomy, and excessive screen time. Option (E) addresses teacher pushback by framing the t...
The district faces a need to balance student achievement gaps and teacher concerns with budget constraints and parent anxiety regarding screen time. Option (E) addresses the "uneven effects" and "teacher pushback" by ensuring the tool supports rather than repl...
Option (E) addresses the core conflicts by ensuring AI remains a supplemental tool, protecting teacher autonomy and small-group instruction while limiting screen-time concerns through usage caps. This hybrid approach balances the personalized learning benefits...
This strategy addresses the primary concerns regarding teacher autonomy and screen time by ensuring that the AI tool remains a supplement to, rather than a replacement for, human instruction. By capping usage hours, it limits screen-time exposure and mitigates...
Timings
Stages
Errors