Measuring Student Potential—with Genetics

How DNA analysis may be able to predict education attainment

Michael J. Petrilli

Education Next

Measuring Student Potential—with Genetics A 600‑Word Summary for Educators
By Michael J. Petrilli · Education Next, June 24, 2025

🌱 Introduction: A Nuanced View of Ability

Drawing from Dalton Conley’s The Social Genome, Petrilli reflects on the long-standing belief in innate ability. He notes President Johnson’s 1965 promise that every child should reach as far as their ability allows. Today, genetic research confirms that both nature and nurture shape educational outcomes—our genes don’t just dictate traits; they interact dynamically with environments.

🧬 The Science of Polygenic Indices (PGIs)

• Polygenic Indices (PGIs) aggregate small genetic variants across thousands of genes.

• Originally used to predict traits like height, they now extend to education.

• Early education PGIs (circa 2013) explained only ~3% of variance in educational attainment. A 2022 version now explains ~16%, or roughly 40% of the heritable component (40% total heritability).

• These scores are most accurate at extremes: top 10%—~71% chance of earning a degree; bottom 10%—~7% chance.

While predictive power remains weak in the middle ranges, extremes offer noteworthy signals.

⚠️ Ethical and Equity Concerns

• Limitations by race/ethnicity: Existing PGIs are developed from predominantly white samples—raising concerns about applicability and fairness.

• Self‑fulfilling biases: Sharing low PGI scores might harm students' expectations. Teachers could unknowingly lower expectations, replicating the Pygmalion effect .

• Environmental confounds: PGIs may inadvertently mirror socioeconomic status (SES). For example, both height and education are influenced by nutrition and family resources, which complicates interpretation.

Robust regulation and thoughtful deployment are essential before any educational use.

💡 Potential Positive Applications

Despite risks, education PGIs may help in:

1. Identifying under-recognized gifted students—especially in underserved populations where standardized testing may underrepresent ability.

2. Guiding resource allocation by flagging students likely to benefit from enrichment or acceleration—even if test scores don’t capture their potential.

3. Evaluating policy effectiveness, e.g., comparing actual attainment to PGI predictions to assess whether environments are supporting or stunting potential.

However, use must proceed cautiously to avoid misuse or bias.

🛤️ Implications for Educators

• Awareness without determinism: PGIs are probabilistic—not prescriptions. They offer insights into potential, not guarantees.

• Equity as a priority: Policies should ensure PGIs don’t reinforce SES or racial disparities.

• Supporting high‑potential students: When paired with evidence‑based practices, PGIs might expand access to accelerated programs for students overlooked by traditional metrics.

🎯 Final Thoughts

The rise of genetics-informed metrics like PGIs is transforming how we understand student potential. While recognizing that ability varies, Petrilli urges educators to uphold Johnson’s vision—supporting every child in reaching their fullest potential. PGIs could help refine talent identification and evaluation of educational systems, but only under strict ethical oversight. As genetic tools advance, educators must engage thoughtfully—balancing hope with caution, potential with fairness.

Original Article

Source: https://www.educationnext.org/measuring-student-potential-with-gene...

------------------------------

Prepared with the assistance of AI software

OpenAI. (2025). ChatGPT (4) [Large language model]. https://chat.openai.com