What Should Perplexed Educators Do? Insights from MIT

Key Points (For Readers in a Rush)

• MIT’s A Guide to AI in Schools: Perspectives for the Perplexed provides grounded, educator-centered insights at a time when AI adoption is outpacing policy and training.

• MIT emphasizes that AI tools can infer personal details and may collect interaction histories, reinforcing earlier discussions about why redaction alone does not protect student information.

• The included AI Resource Vetting Rubric offers a structured way for districts and schools to evaluate tools before adoption. This resource is amazing.

• Practitioners across roles—school psychologists, teachers, counselors, special educators, related service providers, and administrators—benefit from shared norms, clear workflows, and thoughtful use of AI.

Across classrooms, counseling offices, intervention teams, and administrative departments, AI is showing up faster than most educators can respond. Students are experimenting with tools independently, some districts are writing policies for the first time, and practitioners are trying to understand how to use AI both ethically and effectively.

MIT’s A Guide to AI in Schools: Perspectives for the Perplexed was created to help educators navigate these pressures. The guide draws on interviews with over 100 educators and students to explore various perspectives:

Key Challenges Identified

The guide is organized around the core uncertainties educators face. Several themes emerge consistently across the interviews and case studies MIT presents:

1. Privacy and Unintended Information Disclosure

Privacy risks extend beyond the exact words a student or educator types. Modern AI systems are capable of inferring characteristics such as age or location from small amounts of text. In addition, AI tools may retain full histories of user interactions, and some companies may reuse or sell these data for commercial purposes. These concerns mirror my earlier discussions about why redaction alone does not protect student information.

2. Student Learning: Opportunities and Risks

Some educators describe benefits such as scaffolding complex tasks, offering differentiated prompts, or helping students troubleshoot academic hurdles. Others report challenges, including over-reliance on AI-generated answers, reduced depth of understanding, or increased difficulty identifying authentic student work.

Existing research is mixed and still developing. Learning impacts vary by context, by task, and by how teachers structure the use of AI. Educators should approach integration with curiosity, caution, and a commitment to preserving core learning processes.

3. Student Wellbeing and Attention

Multiple educator perspectives are provided on how digital tools influence students socially, emotionally, and behaviorally. Concerns related to attention span, emotional regulation, and the broader effects of increasing screen exposure during the school day are noted. Some note that reduced screen time contributes to calmer and more engaged classroom environments.

This guide does not offer a prescriptive interpretation of these experiences, but it treats them as meaningful insights that should inform local decision-making. Schools benefit from monitoring how AI use intersects with student wellbeing and making choices that prioritize engagement, balance, and healthy learning environments.

4. Inconsistent Expectations and Policy Confusion

This guide highlights the confusion students experience when expectations for AI use vary widely from one class or teacher to another. Without shared norms, students struggle to understand what counts as appropriate help, what requires citation or disclosure, and where the line falls between learning support and academic dishonesty.

This inconsistency also appears at the district level, where policies may be absent, unclear, or not well aligned with current tools. This guide emphasizes that schools should work toward shared, transparent expectations that can be communicated across classrooms, grade levels, and departments.

Resources MIT Provides That Educators Should Know

This guide includes a set of practical frameworks and tools that districts and educators can apply immediately.

1. AI Resource Vetting Rubric

MIT offers a detailed rubric that districts can use to evaluate AI tools before introducing them into classrooms or student services. The rubric assesses areas such as:

• privacy and data security

• age-appropriateness

• educational alignment

• accessibility

• content quality

• user experience

• technical support

• participation in the Student Data Privacy Consortium

My commentary:
This vetting structure pairs well with professional evaluation checklists used in psychology and education. It provides a systematic, evidence-informed way to review AI tools and is directly applicable to school psychologists, related service providers, and administrators seeking a defensible approach to technology adoption.

2. Additional Implementation Resources

MIT also identifies complementary toolkits and policy frameworks that provide guidance on:

• classroom norms for AI use

• district policy development

• risk identification and classification

• emerging state-level approaches

This guide encourages districts to view these materials as evolving resources rather than settled guidance. They serve as starting points for conversation, policymaking, and local experimentation.

How You Can Use MIT’s Insights

Although MIT does not issue formal recommendations, several practical themes emerge from the guide and are highly relevant to school-based professionals.

Make Privacy a Central Consideration

Given AI’s ability to infer personal characteristics and retain interaction histories, privacy should be treated as a foundational aspect of implementation. Schools should use structured vetting tools, avoid unnecessary input of student information, and ensure that staff and students understand the privacy implications of various tools.

Establish Clear Expectations for Student Use

Because much confusion stems from unclear norms, educators benefit from establishing explicit expectations regarding acceptable use, boundaries, and disclosure requirements. Engaging students in discussions about these expectations improves transparency and reduces misuse.

Use AI to Support, Not Replace, Human Expertise

Educators in MIT’s case studies frequently describe using AI to streamline tasks such as brainstorming, differentiation, and planning. MIT’s framing underscores that AI works best when it supports skilled human judgment rather than automating tasks that rely on professional expertise.

Build Policy Through Collaborative Dialogue

MIT emphasizes the importance of involving educators and students in the development of policies and norms. Shared understanding leads to greater consistency across classrooms and more effective adoption of tools. Policies should evolve over time as evidence grows and tools change.

Final Takeaways

MIT’s Perspectives for the Perplexed highlights a moment of rapid change in schools. For school psychologists, teachers, counselors, special educators, administrators, and related service providers, several messages are clear:

• AI can enhance professional work when used thoughtfully.

• Privacy protections must extend beyond redaction and include strong vetting and workflow design.

• Student wellbeing and engagement should guide decisions about implementation.

• Clear and consistent expectations help students use AI responsibly.

• Educators, not vendors, should shape how AI is used in schools.

AI Use Disclosure

Portions of this post were drafted with the assistance of an AI writing tool and revised by the author for accuracy, clarity, and professional judgment.