D.C. Voices: Artificial Intelligence (AI) in public education – D.C. Policy Center

Report on the Integration of Artificial Intelligence in Public Education and its Alignment with Sustainable Development Goals

The integration of Artificial Intelligence (AI) into the public education sector presents a significant opportunity to advance Sustainable Development Goal 4 (SDG 4: Quality Education). However, its implementation must be managed strategically to mitigate risks and address inequities, in line with SDG 10 (Reduced Inequalities) and SDG 16 (Peace, Justice, and Strong Institutions). This report analyzes the current landscape, stakeholder perspectives, and frameworks for harnessing AI to enhance learning outcomes for all students.

Current Landscape: AI Adoption and Equity Concerns

The adoption of AI in education is accelerating, with stakeholders acknowledging its permanence. The primary challenge is to ensure its application supports authentic learning and ethical standards. This aligns with the objective of SDG 4 to ensure inclusive and equitable quality education.

Training and Usage Disparities

Recent data indicates a significant gap in teacher training, which poses a threat to achieving SDG 10. Key statistics include:

• As of Fall 2024, approximately 50% of school districts provided AI training to teachers, a 100% increase from the previous year.
• A notable disparity exists, with 67% of low-poverty districts providing training, highlighting an equity gap that could exacerbate educational inequalities.
• A 2023-24 survey indicated that 38% of school staff use AI tools on a daily or weekly basis, with 48% reporting increased efficiency and productivity. This efficiency can free up resources to focus on quality instruction, supporting SDG 4.

Frameworks for Responsible and Equitable AI Integration

To guide the responsible integration of AI, organizations like AI for Equity are developing best practice frameworks. Such initiatives are crucial for building strong and accountable educational institutions (SDG 16) that can navigate technological change. The AI for Equity framework for school leadership includes eight key components designed to foster an adaptive, multi-year process that continually reassesses pedagogy in light of technological advancements.

This structured approach is vital for closing the equity gap, ensuring that AI integration promotes, rather than hinders, the goals of SDG 10 by providing all students with access to meaningful AI learning experiences, regardless of their district’s resources. This prepares them for future labor markets, contributing to SDG 8 (Decent Work and Economic Growth).

Stakeholder Analysis on AI Implementation

Ashley Jeffrey, Chief Strategy Officer, Washington Leadership Academy PCS

Washington Leadership Academy (WLA) exemplifies a strategic approach to AI that prioritizes equity. Their implementation focuses on:

1. Ethical Considerations: Centering conversations on the racial, environmental, and social implications of AI to foster an equity mindset, directly addressing the core principles of SDG 10.
2. Structured Use: Employing clear guardrails and rubrics to define the appropriate use of AI in assignments, ensuring it serves as a tool to enhance critical thinking rather than replace it, a key tenet of SDG 4.
3. Partnerships: Collaborating with external organizations to learn best practices, ensuring the responsible integration of technology and contributing to robust educational systems (SDG 16).

Janie Scanlon, Founding Partner, Elevant Strategies

Scanlon emphasizes the need for strong institutional oversight to ensure AI contributes positively to educational goals. Key points include:

• System-Level Governance: Advocating for clear, system-level data governance structures and policies to manage data privacy and safety concerns, which is fundamental to building the strong institutions required by SDG 16.
• Problem-Oriented Approach: Stating that AI initiatives must be tied to tangible processes and realistic outcomes, whether improving operational efficiency or exposing students to STEM pathways, thereby supporting SDG 4 and SDG 9 (Industry, Innovation, and Infrastructure).
• Shared Responsibility: Arguing against placing the burden of vetting AI tools solely on educators, highlighting the need for institutional support to ensure ethical and effective use.

Aaron Cuny, Founder & CEO, AI for Equity

Cuny highlights the emerging equity challenge in AI education, linking it directly to long-term economic outcomes.

• Addressing Inequality (SDG 10): Noting that higher-poverty districts are less likely to provide structured AI learning, creating a gap in AI fluency that will impact future employment opportunities and economic growth (SDG 8).
• Roadmap for Integration: Recommending that district leaders develop clear roadmaps that connect AI use to academic goals, align professional learning, and build partnerships. This strategic planning is essential for achieving SDG 4 at scale.
• Measurement and Benchmarking: Pointing to tools like The AI Innovation Index that help systems measure progress and make strategic decisions to ensure equitable implementation.

Samuel Price, Senior, Washington Leadership Academy

A student perspective reveals the practical benefits and challenges of AI in the classroom, reflecting the core targets of SDG 4.

1. Enhancing Learning: AI is a strength for generating ideas and conceptualizing difficult topics, acting as a starting point that allows students to build upon and create their own work.
2. Promoting Critical Thinking: The primary challenge is the risk of over-reliance, which can diminish the ability to think critically. School restrictions on AI use for planning and outlining only are a crucial measure to safeguard this essential component of a quality education.
3. Limitations: AI can produce non-diverse or similar responses for multiple students, a limitation that underscores the need for students to develop their own unique insights.

Analysis of SDGs, Targets, and Indicators

1. Which SDGs are addressed or connected to the issues highlighted in the article?

The article on the integration of Artificial Intelligence (AI) in public education touches upon several Sustainable Development Goals (SDGs). The analysis identifies the following key SDGs as being directly relevant to the themes discussed:

• SDG 4: Quality Education

  This is the most prominent SDG in the article. The entire text revolves around ensuring inclusive and equitable quality education by leveraging AI. It discusses enhancing learning, developing new skills, providing teacher training, and establishing policies for the responsible use of technology in classrooms.

• SDG 10: Reduced Inequalities

  The article explicitly raises concerns about equity. It highlights an “emerging equity challenge” where students in higher-poverty districts have less access to AI learning opportunities and teacher training compared to their peers in lower-poverty districts. This directly connects to the goal of reducing inequalities within and among countries.

• SDG 8: Decent Work and Economic Growth

  The article links AI education to future employment prospects. It states that “today’s students will graduate into workplaces where AI fluency is a baseline expectation” and emphasizes the goal of building “skills that will prepare them for the workforce of tomorrow.” This aligns with the SDG’s aim to promote productive employment and decent work for all.

• SDG 17: Partnerships for the Goals

  The importance of collaboration is a recurring theme. The article mentions that “Strong partnerships have been essential” and cites examples of schools working with non-profits like “AI for Equity, The Learning Accelerator, and Leading Educators” to successfully implement AI, which is central to the spirit of SDG 17.

2. What specific targets under those SDGs can be identified based on the article’s content?

Based on the article’s discussion, several specific SDG targets can be identified:

1. Target 4.4 (under SDG 4)

   “By 2030, substantially increase the number of youth and adults who have relevant skills, including technical and vocational skills, for employment, decent jobs and entrepreneurship.” The article directly supports this target by emphasizing the need to help students “build digital literacy and critical thinking skills that will prepare them for the workforce of tomorrow,” where AI fluency is a required skill.

2. Target 4.c (under SDG 4)

   “By 2030, substantially increase the supply of qualified teachers…” The article addresses this by focusing on teacher training. It notes that as of fall 2024, “roughly half of school districts reported that they have provided training to their teachers about generative artificial intelligence tools,” highlighting the ongoing effort to equip educators with the necessary skills to teach effectively in an AI-driven environment.

3. Target 10.3 (under SDG 10)

   “Ensure equal opportunity and reduce inequalities of outcome…” The article points to a direct challenge to this target by stating, “higher-poverty districts are less likely than their lower-poverty peers to provide structured AI learning opportunities for both staff and students.” Addressing this disparity is a central theme.

4. Target 8.6 (under SDG 8)

   “By 2020, substantially reduce the proportion of youth not in employment, education or training.” Although the target date has passed, its principle remains relevant. The article’s focus on preparing students for a labor market that demands AI skills is a direct strategy to ensure they are equipped for future employment and are not left out of the workforce.

5. Target 17.17 (under SDG 17)

   “Encourage and promote effective public, public-private and civil society partnerships…” The article provides a clear example of this target in action, describing how Washington Leadership Academy’s “collaborations with organizations like AI for Equity, The Learning Accelerator, and Leading Educators have helped us thoughtfully integrate new tools into our classrooms.”

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

Yes, the article mentions several quantitative and qualitative indicators that can be used to measure progress:

• Proportion of school districts providing AI training to teachers: The article provides a direct metric: “roughly half of school districts reported that they have provided training to their teachers about generative artificial intelligence tools.” This can be tracked over time to measure progress under Target 4.c.
• Disparity in teacher training by district poverty level: An indicator for Target 10.3 is the gap in training provision. The article specifies that “67 percent of low-poverty districts reported having provided training for teachers,” which serves as a benchmark to measure against high-poverty districts and track the reduction of this inequality.
• Rate of AI adoption by education staff: The article cites a survey from AI for Equity, which found that “38 percent of staff said they are using AI tools on a daily/weekly basis.” This indicator measures the extent to which AI is being integrated into daily educational practices.
• Perceived impact on productivity: A qualitative indicator is mentioned where “all respondents stated that generative AI has helped become more efficient and/or productive (48 percent).” This helps measure the perceived value and effectiveness of AI tools among educators.
• The AI Innovation Index: The article explicitly mentions this tool, which is designed to help school systems “measure progress against their roadmap actions, benchmark their approaches against peers, and surface insights that guide more equitable, strategic decision-making over time.” This represents a composite set of indicators for tracking progress in AI integration and equity.

SDGs, Targets and Indicators

Source: dcpolicycenter.org