https://www.sreb.org/ai-rec-8-procurement Policy Recommendation 8, Commission on AI in Education en https://www.sreb.org/post/ai-procurement-questions <div class="field field-type-text field-field-subtitle"> <div class="field-items"> <div class="field-item odd"> For States, K-12 Districts and Postsecondary Institutions  </div> </div> </div> <p> These&nbsp;questions were written to help create guidelines for the procurement, implementation and evaluation of artificial intelligence technologies in K-12 districts and institutions. These questions should help ensure that the AI tools purchased align with educational goals, comply with legal standards&nbsp;and promote ethical use.&nbsp; </p> <h4> Questions for Districts and Postsecondary Institutions&nbsp; </h4> <p> Does the AI product align with your education goals?&nbsp; </p> <p> Have you verified that the AI product in question supports ethical use?&nbsp;&nbsp; </p><p><a href="https://www.sreb.org/post/ai-procurement-questions" target="_blank">read more</a></p> Post AI Rec 8 Procurement Thu, 12 Jun 2025 18:56:00 +0000 Beth Day 8885 at https://www.sreb.org https://www.sreb.org/post/k-12-ai-use-cases <div class="field field-type-text field-field-subtitle"> <div class="field-items"> <div class="field-item odd"> District-Wide Procurement Examples </div> </div> </div> <h4> Use Case 1: Early Warning System </h4> <h5> Purchasing an early warning system that can predict which students are at risk of academic failure or dropping out using historical and real-time school data with an AI application such as BrightBytes or PowerSchool Predict.&nbsp; </h5> <h6> Users </h6> <p> Administrators, counselors, MTSS teams.&nbsp; </p> <h6> Scenario </h6> <p> An AI system that analyzes attendance, behavior, grades and engagement patterns, and then flags students for intervention with recommendations for tiered support.&nbsp; </p> <h6> Benefits </h6> <p> Early identification, more proactive interventions, reduced dropout rates.&nbsp; </p> <h6> Risks </h6> <p> Data bias, student profiling, transparency of predictions. &nbsp; </p> <h6> Metrics </h6> <p> Intervention rates, graduation rates, number of false positives and negatives.&nbsp; </p> <h4> Use-Case 2 </h4> <h5> Purchasing an AI tutoring and homework application that could extend learning time with 24/7 AI support for students, such as Khanmigo or Squirrel AI.&nbsp; </h5> <h6> Users </h6> <p> Teachers and students&nbsp; </p> <h6> Scenario </h6> <p> Teachers use AI data to adjust instruction and provide personalized support for each student.&nbsp;&nbsp; </p> <h6> Benefit </h6> <p> Increased access, student engagement, personalized learning paths.&nbsp; </p> <h6> Risk </h6> <p> Tool accuracy, digital divide, students AI dependency&nbsp;&nbsp; </p> <h6> Metrics </h6> <p> Usage frequency, student achievement growth, user satisfaction&nbsp; </p> <h4> Use Case 3: IEP Assistant </h4> <h5> Purchasing an automated individualized education program, or IEP, development assistant that could support special education teams in drafting individualized education plans more efficiently, such as AI-enabled IEP generators like AbleDocs AI and AI-powered templates.&nbsp; </h5> <h6> Users </h6> <p> Special education teachers and case managers&nbsp; </p> <h6> Scenario </h6> <p> Teachers will input baseline data and goals and then the system will suggest language, accommodations and goal structures </p> <h6> Benefits </h6> <p> Reduced paperwork time, improved compliance, more consistent language&nbsp; </p> <h6> Risks </h6> <p> Misalignment with student needs, over-reliance, legal accuracy </p> <h6> Metrics </h6> <p> IEP completion time, compliance audit rates, teacher satisfaction </p> <h4> Use Case 4: AI-Assisted Teaching Practice </h4> <h5> Purchasing an AI-assisted professional learning application that would recommend targeted professional development based on teacher practice and student performance data, such as SWIVL, Learn Platform, Coursera for Districts with AI plugins.&nbsp; </h5> <h6> Users </h6> <p> Teachers and professional development coordinators </p> <h6> Scenario &nbsp; </h6> <p> System maps each teacher’s needs to available PD modules and then provides micro-credentials aligned to the district education and AI goals </p> <h6> Benefits </h6> <p> Targeted growth, efficient PD investments, personalized learning </p> <h6> Risks </h6> <p> Data privacy, relevance of recommendations, algorithmic bias </p> <h6> Metrics </h6> <p> PD completion rates, teacher growth measures, impact on classroom outcomes </p> <h4> Use Case 5: Chatbot for K-12 Parents </h4> <h5> AI chatbot for district communication to answer common parent and student questions using natural language processing, such as&nbsp;Ada and Intercom AI.&nbsp; </h5> <h6> Users </h6> <p> Parents, students, office staff, communications teams </p> <h6> Scenario </h6> <p> Parents use chatbots to answer questions about things like bus schedules, policies, lunch menus. The application would escalate complex queries to human staff </p> <h6> Benefits </h6> <p> 24/7 support, reduced staff burden, multilingual access </p> <h6> Risks &nbsp; </h6> <p> Inaccurate responses, accessibility gaps, limited nuance </p> <h6> Metrics </h6> <p> Number of queries resolved, response time, family satisfaction surveys </p><p><a href="https://www.sreb.org/post/k-12-ai-use-cases" target="_blank">read more</a></p> Post AI Rec 8 Procurement Thu, 12 Jun 2025 18:51:00 +0000 Beth Day 8884 at https://www.sreb.org