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Back to “Reality”: A Review of Cheng Lin’s RoboHumanities: Imaginations, Narratives, and Ethics regarding Robots

Back to “Reality”:A Review of Cheng Lin’s RoboHumanities: Imaginations, Narratives, and Ethics regarding Robots

Other International lawGlobalin force

This document reviews Cheng Lin’s work on RoboHumanities, examining the intersections of AI, robotics, narratives, ethics, and philosophy through a collection of thirteen specialized essays. It focuses on the pedagogical, philosophical, and ontological shifts brought about by generative artificial intelligence and machine learning in human environments. The papers explore the history of educational automation, the cognitive role of digital interfaces, and critical competence regarding automated systems.

Summary and content fields are drawn from the document text and are pending editorial review.

Who issued it

Peter the Great St. Petersburg Polytechnic UniversityAcademic · RU
Publisher
Legal forceAspirational
Under whose authorityInternational law
Judged fromabstract

Who it governs

Researchers and academicsGeneral public

Researchers and academics interested in the philosophical, educational, and cultural implications of generative artificial intelligence and machine learning.

Sectors
Research and science · academic publishing

What the document contains

What's covered

The text covers a review of RoboHumanities, examining how machine learning and generative artificial intelligence intersect with language pedagogy, philosophy of technology, historical automation, and epistemic agency.

What's missing

The document does not provide binding legislative regulations, enforcement mechanisms, or commercial procurement guidelines.

Key takeaways
  • Generative AI forces a reexamination of human epistemic agency, judgment, and educational automation.
  • Historical precedents such as Ramon Llull's combinatorial machines highlight the ongoing quest to automate thought and language.
  • The role of the digital interface extends beyond a neutral conduit, serving as an active semantic matrix that structures cognitive operations.
  • Evaluating AI-generated output requires distinguishing between successful task performance and true conceptual understanding.
  • Interdisciplinary perspectives from philosophy, literature, and educational theory are essential for addressing the challenges of ubiquitous AI.
Outline
· Special Topic: Machine Learning for Learning Machines
· Editorial introduction
· Research articles on educational automation, cognitive interfaces, lemmatic analysis, and generative AI models
· Critical reflections on Ramon Llull's thinking machine and contemporary large language models
· Book reviews and contributed papers
Issued21 September 2026
Length117,713 words
FormatNot recorded
Scopeglobal

How it scores

Pending review

Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.

Accountability

1/5

Clear responsibility for AI outcomes

taking responsibility for what is said

Conclusion: Teaching Machines and Human Learning

screening score · 2026-10-23

Fairness

0/5

Non-discrimination and equitable treatment

n/a

screening score · 2026-10-23

Human Dignity

2/5

Respect for the inherent worth of persons

the priority in education should remain the cultivation of human agency and judgement

Abstract

screening score · 2026-10-23

Privacy

0/5

Protection of personal data and private life

n/a

screening score · 2026-10-23

Transparency

2/5

Openness about how AI systems work and are used

Transparency, fairness and human judgement to academic integrity protocols

Large Language Models: From Programmed Instruction to Generative Interaction

screening score · 2026-10-23

Enforcement

0/5

Consequences and compliance mechanisms with real force

n/a

screening score · 2026-10-23

Impact Assessment

0/5

Required assessment of an AI system's risks/impacts before or during use

n/a

screening score · 2026-10-23

Topics

Physical AI Governancecontext
generative_ai philosophy_of_technology language_pedagogy combinatorics epistemic_agency machine_learning