AI Rec 8 Procurement

States should work with districts and with postsecondary systems and institutions to create or update strong procurement policies to incorporate AI. 

Effective procurement policies will help districts and institutions identify AI tools that align with their education goals, define personnel roles and responsibilities, ensure transparency and fairness, streamline purchasing processes, promote cost-effectiveness, mitigate risk, create sustainability, and support continuous improvement. 

Streamline Procurement 

The procurement of AI tools in education is complicated by bureaucratic hurdles, lengthy approval cycles, and a lack of AI expertise among school administrators. Many districts and institutions may rely on traditional procurement policies that may not adequately address AI-specific risks, such as algorithmic transparency and ethical concerns. To streamline procurement, districts and institutions need clear guidelines, expert consultation, and standardized evaluation criteria to assess AI solutions based on both cost-effectiveness and pedagogical impact. 

Cost of Ownership 

Like other technology purchasing, AI procurement extends beyond the purchase cost or licensing fees. As result, districts and institutions need to carefully evaluate AI purchases and related costs beforehand to establish a more accurate cost of ownership over time.  

What is the cost of implementation, training, updates and integration with existing systems? Will you need new hardware or additional infrastructure to accommodate the AI tools? Will there be any additional cybersecurity costs? What about the cost of future updates?  

Failure to answer these questions and account for other costs could lead to unexpected budget overruns that may require abandoning the product and having to start over with a new product. To avoid this, budget appraisals should project any expenses over time to ensure long-term affordability and sustainability based on your projected revenue. Procurement planning must include processes to determine the total cost of ownership: licensing, training, maintenance and scalability. Accordingly, districts and postsecondary institutions should insist on transparent pricing models from vendors which will help them avoid possible hidden costs. 

Sustainability 

Districts and institutions must identify reliable funding sources to sustain AI investments over time. Historically, federal and state grants have helped, but these funding sources are often short-term and may not cover ongoing costs such as maintenance and staff training.

District and institution AI procurement plans should include multi-year funding strategies that factor in potential fluctuations in financial resources to avoid disruptions. These plans should be reviewed regularly to adjust for unanticipated costs, price increases and budgetary changes. 

Product Lock-in 

Customer dependency on any single vendor or sole product — also known as vendor lock-in or proprietary lock-in – could result in your district or institution facing substantial switching costs if you change to another vendor or product in the future. 

AI tools often use exclusive data formats and closed-source algorithms, making integration with other educational platforms challenging. If you decide to switch vendors at a future date for whatever reason, you may face high migration costs, data loss, along with the cost of retraining staff.

To mitigate this concern, districts and institutions should prioritize AI tools that support open standards, ensuring long-term flexibility and cost efficiency in procurement. 

AI Effectiveness 

Quantifying the return on investment for AI in education will be complex because the benefits are often qualitative rather than purely financial. Procurement plans should include ongoing monitoring and feedback loops that will inform policy, practice, impact analysis, effectiveness, compliance and fairness.  

AI’s success in education should be based, in part, on student engagement, student outcomes, personalization of learning, teacher practice and administrative efficiency. Without clear performance metrics, school boards may struggle to justify continued investments. Procurement processes should include pilot programs and data-driven evaluations that inform full implementation efforts. 

Data Privacy and Security 

AI systems require access to sensitive student and teacher data, raising serious concerns about data privacy, cybersecurity and legal compliance. Districts and institutions must adhere to federal privacy laws and regulations such as FERPA, the Family Educational Rights and Privacy Act, and COPPA, the Children’s Online Privacy Protection Rule. These laws and regulations are designed to ensure that AI vendors handle data securely and ethically.

Poorly secured AI systems can become targets for cyberattacks that jeopardize student data, unauthorized tracking, and breaches of confidential information. When procuring AI solutions, districts and institutions should implement transparent data policies, encryption standards, and compliance with national and state regulations to safeguard student information. 

Equal Access 

AI has the potential to enhance learning, but ensuring access to AI across states will require commitment and resources. To ensure AI benefits all learners, procurement strategies should emphasize affordable, adaptable and accessible AI solutions that comply with universal design principles and address digital divide concerns. 

Teacher Training and Change Management 

Even the most advanced AI tools are ineffective if educators are not trained to use them. Successful AI implementation requires teacher professional development programs that cover both technical and pedagogical aspects of AI-driven education. Some educators may be reluctant to adopt AI over concerns about job displacement, loss of instructional autonomy, or steep learning curves.

Without ongoing support and hands-on training, AI initiatives risk low adoption rates and ineffective classroom integration. School districts must allocate resources for continuous training, instructional coaching, and teacher collaboration to ensure AI enhances — rather than replaces — human instruction. 

Transparency 

AI in education must comply with various legal frameworks that govern data privacy, student rights and algorithmic fairness. Regulations such as FERPA, COPPA and the General Data Protection Regulation impose strict requirements on how AI vendors collect, store and use student data. In addition, there is growing demand for greater transparency in AI decision-making, particularly regarding how algorithms impact grading, student evaluations and discipline policies.

Failure to comply with these regulations can result in legal liabilities, financial penalties and loss of public trust. Districts should prioritize AI vendors that provide auditability, transparency and clear documentation to ensure compliance with evolving legal standards. 

Safeguards and Support 

Procurement plans should also incorporate contractual safeguards that include explicit data ownership and retention policies, regular third-party audits of AI systems, and clear liability clauses in case of malfunctions or data breaches. Make sure that AI tools integrate with your existing learning management system, student information system and information technology. Require vendors to disclose bias mitigation strategies. 

Districts and institutions should also incorporate requirements for ongoing support, software updates and professional development. Ask potential users such as teachers, faculty students, administrators and IT leaders to take part in your decision-making loops, tests-of-concept and pilot programs before making final purchasing decisions.  

References 

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