SPECIAL REPORT
cords requests and contract reviews, reinforcing the “bad data in, bad data out” principle. He advocated for robust policies like Broken Arrow’s Vision and Mission. Singh clarified that true AI synthesizes multi-source
data for actionable insights, distinguishing it from mere automation and recommended retrieval augmented gen- eration (RAG) for fact-based outputs. The panel outlined essential AI safeguards: Exclusive use of IT-approved and licensed tools, multi-factor authentication, and strict avoidance of uploading confidential or personally identi- fiable information. The panelists detailed the many ways AI is being
integrated into school transportation operations, from predictive maintenance, vehicle diagnostics, telematics, and fleet management to dispatch automation, field trip planning, attendance tracking, and master bell sched- ule optimization. They also highlighted applications in driver and employee performance, including driver coaching, behind-the-wheel assessments, accident analysis, training plan development, safety modules, and automated performance reviews. Beyond daily operations, panelists described using AI
to support budgeting, identify contract issues, research laws and regulations, analyze public documents and district-specific data, plan travel, develop parent com- munication templates, and even compare social media claims with more reliable information. A recurring theme was the importance of “context en- gineering,” or providing AI tools with detailed, relevant instructions. Panelists recommended using prompts of roughly 300 words tailored to a district’s specific needs. They also encouraged gradual staff adoption, internal model training and continued human review to reduce errors and prevent AI-generated misinformation or “hallucinations.” Survey data of 23 STN EXPO attendees found that 65
percent of respondents use AI at least occasionally or daily, with ChatGPT dominating use. Eighty-two percent of respondents are using it for writing emails/communi- cations. Whereas 59 percent are using AI for customer/ parent communication, 45 percent for policy or doc- ument drafting, 41 percent for training or onboarding materials. The percentage falls to 32 percent for data analysis and reporting. The lowest use cases are data- base/app development (14 percent) and routing/logistics support (9 percent). Plus, the survey found that most dis- tricts are still in “Wild West” mode with only 33 percent having formal organization-wide guidance and policies. Lack of training at 68 percent is the main driver for hold- ing transportation employees back from using AI. Cathy Whitney, transportation coordinator at Glenn
County Office of Education in California, shared with School Transportation News after the conference that she is still new to use of AI. She said she doesn’t believe her
24 School Transportation News • SEPTEMBER 2026
organization has any policies or guidance on use cases. “I’m looking forward to seeing how it can help and
improve on what we’re already doing that may save time and save the funds for other needed items. It’s exciting to see how fast it’s growing and the things that it can do in a very short time,” she added. However, her biggest fear is getting the wrong infor-
mation and using it in a report or a way that presents false information. “When I was at the STN [EXPO] con- ference, they kept saying check and make sure that the information is correct. We already do that in our regular lives every day,” Whitney added. “I think a lot of people are unsure or aren’t good with change and [fear] that AI is going to take jobs away from other people. I think they would have to see the positive side and see how it helps them in their lives and jobs to save time.” Her takeaway from the sessions was to learn more
about AI. “I want to create and gather data by putting information into AI that would [normally] take me hours to do and have it create it in a very short time. I look for- ward to a future with AI in it,” she concluded. Meanwhile, Keba Baldwin, the director of transpor-
tation and central garage for Prince George’s County Schools in Maryland, shared on the School Transpor- tation Nation podcast that his district has adopted AI policies and is cautious about its AI use cases. He said transportation is unable to use ChatGPT or Claude be- cause they don’t meet the district’s security standards. In a year’s time, PGCPS has expanded from using
AI for help crafting emails to now analyzing data. “I’m thankful it’s helping to cut some of our workload in half,” he said on Episode 319. “But the technology is still devel- oping, and we just want to keep up with it.” When navigating vendors who say they have AI
integrated into their technology suites, Baldwin ad- vised asking the right questions. “At the end of the day, I need to know if their infrastructure can align with my infrastructure to get us the outcome,” he said, adding that some companies might not even meet the district security measures, and student transporters should be aware of that. “How much time it’s going to take to implement?
What’s the customer care on that? Can I call you 24/7?” he said of some other questions to ask. Additionally, he said technology demos are great but
remain at surface level. When implementing new tech- nology, he advised directors to get into the weeds of the offerings. ●
Listen to Episode 319 of the School Transportation Nation podcast to hear from reigning
Transportation Director of the Year Keba Baldwin on this past year’s “Hot Takes.”
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76