Deep ASI Literacy
Educating for Alignment with Artificial Super Intelligent Systems
Bibliographic Data
| ID | 21314520 |
|---|---|
| Authors | Nicolas Tanchuk (0000-0002-5187-8003, University of Illinois Urbana-Champaign, corresponding author), Nicolas J Tanchuk (0009-0000-6988-9731, Department of Education Policy, Organization and Leadership University of Illinois at Urbana‐Champaign) |
| Year | 2025 |
| Volume | 75 |
| Issue | 4 |
| Pages | 739-764 |
| Publication date | 2025-08-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Educational Theory (JOURNAL) |
| Journal identifiers | ISSN: 0013-2004 • E-ISSN: 1741-5446 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/edth.70030 |
| OpenAlex | W4411129097 |
| Language | EN |
| Citations received | 4 |
| References cited | 1 |
Artificial intelligence companies and researchers are currently working to create Artificial Superintelligence (ASI): AI systems that significantly exceed human problem‐solving speed, power, and precision across the full range of human solvable problems. Some have claimed that achieving ASI — for better or worse — would be the most significant event in human history and the last problem humanity would need to solve. In this essay Nicolas Tanchuk argues that current AI literacy frameworks and educational practices are inadequate for equipping the democratic public to deliberate about ASI design and to assess the existential risks of such technologies. He proposes that a systematic educational effort toward what he calls “Deep ASI Literacy” is needed to democratically evaluate possible ASI futures. Deep ASI Literacy integrates traditional AI literacy approaches with a deeper analysis of the axiological, epistemic, and ontological questions that are endemic to defining and risk‐assessing pathways to ASI. Tanchuk concludes by recommending research aimed at identifying the assets and needs of educators across educational systems to advance Deep ASI Literacy
Literacy · Mathematics education · Pedagogy · Sociology · Artificial Intelligence · Computer Science · Digital Transformation in Industry · Engineering Education and Technology · Psychology · Robotics and Automated Systems
| Citations per year | 4 |
|---|---|
| Citation span | 2025 - 2026 (2) |
| Highly cited | No |