Annotated Bibliography
Organized by Bridge Checklist panel · 49 sources · TAI-KPI Framework
This bibliography represents the core research foundation of the TAI-KPI framework and the Bridge Checklist. Sources are organized by their primary relevance to each panel, reflecting the interdisciplinary synthesis that distinguishes this approach from conventional AI literacy frameworks.
Each annotation is written for an educated non-specialist reader. For deeper theoretical context, see Theoretical Foundations.
Panel 1 — Gyroscope
Human Regulation, Nervous System, Embodied Cognition, and Contemplative Practice
These sources ground the claim that stabilization precedes interpretation — that human nervous system state is the prerequisite for reliable perception, learning, and ethical action.
Abelson, S., Lipson, S. K., & Eisenberg, D. (2023). What Works for Improving Mental Health in Higher Education? Higher Education: Handbook of Theory and Research. → Reviews effective campus programs to support student mental well-being and resilience.
Broilo, P. et al. (2024). Screen use and mental health. [Journal.] → Investigates how digital information habits impact psychological well-being and stress levels.
De Felice, S., et al. (2025). Relational neuroscience: Insights from hyperscanning research. Neuroscience & Biobehavioral Reviews, 169, 105979. https://doi.org/10.1016/j.neubiorev.2024.105979
Forghani, M. (2025). Consciousness as a Resonance Frequency Coding Principle (RFCP). [Manuscript in preparation.] → Defines consciousness as resonance, offering new ways to measure mental clarity and empathy. Note: manuscript in preparation; cited for theoretical framing.
Geesink, J. H., & Meijer, D. K. F. (2018). Favourable and Unfavourable EMF Frequency Patterns in Cancer. NeuroQuantology. → Explores how specific electromagnetic frequencies affect biological health and cellular order.
Judkins, J. L., Moore, B. A., & Collette, T. (2020). Psychological Hardiness. ResearchGate. → Analyzes how mental resilience helps leaders and groups adapt to high-stress changes.
Kirchhoff, M. (2025). The Idealized Mind. [Journal.] → Examines how internal mental models help humans navigate and predict their environments.
Kuhl, J., Quirin, M., & Koole, S. L. (2020). The Functional Architecture of Human Motivation: Personality Systems Interactions Theory. Advances in Motivation Science, 8. → Models how self-regulation helps people integrate experiences and maintain internal stability.
Meijer, D. K. F. (2023). Concept of Integral Holographic Consciousness. [Journal.] → Connects brain wave coherence to mental health and the processing of information.
Meijer, D. K. F., & Geesink, J. H. (2019). Life and Consciousness are Guided by a Semi-Harmonic EM Background Field. NeuroQuantology. → Proposes that universal frequency patterns influence human consciousness and biological health.
Milyavsky, M. et al. (2018). To Reappraise or Not to Reappraise? Emotion Regulation Choice and Cognitive Energetics. Emotion. → Explains how motivation and mental energy influence how people regulate their emotions.
Reiss, F. et al. (2020). From anxiety to action. [Journal.] → Studies how different psychological defenses influence human behavior during global uncertainty.
Sbitnev, V. (2024). The edge of chaos is that where consciousness manifests itself through intermittent dynamics. Academia Biology. → Argues that human awareness is most adaptive at the boundary of order and chaos.
Singer, T. (2025). A neuroscience perspective on the plasticity of the social and relational brain. Annals of the New York Academy of Sciences, 1547, 52–74. → Shows how specific mental practices can increase compassion and social resilience.
Panel 2 — Radar
Uncertainty, Logic, Misinformation, AI Systems, Information Ethics, and Ecological Costs
These sources ground the claim that widening sensing requires formal tools for holding uncertainty, tracking signal provenance, and making visible the costs that AI systems displace onto workers, communities, and ecosystems.
Bruschi, F., & Diomede, M. (2023). A framework for assessing AI ethics. [Journal.] → Proposes principles like autonomy and harm prevention for ethical AI development.
Butlin, P. et al. (2025). Identifying indicators of consciousness in AI systems. [Journal.] → Proposes rigorous methods to evaluate whether AI systems truly possess awareness.
Çetin, R. B., Stanusch, N., & Faddoul, M. (2025). From "Googling" to "Asking ChatGPT": Governing AI Search. AI Forensics. → Analyzes the risks of AI search engines in controlling and generating information.
Cheng, J. (2025). AI Could Undermine Emerging Economies. [Publication.] → Warns that AI automation may block economic pathways for developing nations.
de Vries-Gao (2026). The carbon and water footprints of data centers. [Journal.] → Reveals the significant environmental costs required to power modern AI systems.
Frontiers Media (2025). Unlocking AI's Untapped Potential. Frontiers Science News. → Advocates for responsible AI research through transparency and better literacy.
Hadgu, A. T. et al. (2023). Combating Harmful Hype in Natural Language Processing. [Conference/Journal.] → Challenges corporate claims about AI capabilities to prevent cultural harm.
Kambhampati, S. et al. (2025). Stop Anthropomorphizing Intermediate Tokens. [Journal/Preprint.] → Warns against treating AI's mathematical outputs as human-like thoughts or reasoning.
Kirmayer, L. J. (2024). Science and sanity: A social epistemology of misinformation. Transcultural Psychiatry, 61(5), 795–808. → Explores how social systems and prior beliefs influence our ability to trust information.
Mehdizadeh, A., & Hilbert, M. (2025). Epistemic Substitution: How AI-generated encyclopedias restructure authority. arXiv. → Shows how AI encyclopedias replace peer-reviewed research with less reliable content.
Smarandache, F. (2025a). Teaching To Measure Doubt With Artificial Intelligence. [Preprint.] → Teaches students to use AI to visualize uncertainty instead of seeking simple answers.
Smarandache, F. (2025b). Transparency in Uncertainty: A neutrosophic evaluation of ethical reasoning in language models. [Preprint.] → Uses non-binary logic to expose internal conflicts and ethical dilemmas within AI models.
Ungruh, R., Al Nahadi, M., & Pera, M. S. (2026). Mirror, Mirror: Exploring Stereotype Presence Among Top-N Recommendations. ACM Transactions on Recommender Systems. → Investigates how AI recommendation systems can reinforce harmful social biases in children.
Panel 3 — Compass
Evolutionary Biology, Cooperation, Institutional Design, Governance, and Educational Equity
These sources ground the claim that kind action is not sentiment but the evolutionarily and institutionally validated structure of systems that persist and flourish — and that AI literacy must be oriented toward collective human agency rather than individual tool mastery.
Collins, H. (2024). Establishing veritocracy: Society, truth and science. Transcultural Psychiatry, 61(5), 783–794. → Advocates for a democracy where truth and scientific honesty guide political power.
EDSAFE AI Alliance (2025). EDSAFE Policy Labs: Policy Stack. [Report.] → Provides a roadmap for schools to use AI safely and fairly.
Gould, S. J. (2024). A Conceptual View of Organizational Evolution. [Journal.] → Models how institutions evolve using biological principles like variation and selection.
Hendrycks, D. (2024). Natural Selection Favors AIs over Humans. [Preprint.] → Argues competitive pressures may evolve AI agents that prioritize power over humans.
Hoel, E. P., Albantakis, L., & Tononi, G. (2013). Quantifying causal emergence shows that macro can beat micro. PNAS, 110(49), 19790–19795. → Mathematically shows how higher-level group dynamics can supersede individual actions.
Ito, M. et al. (2020). Social Media and Youth Wellbeing. [Report/Journal.] → Discusses how online connections and shared interests fuel learning and belonging.
Jansma, A., & Hoel, E. (2025). Engineering Emergence. [Preprint.] → Discusses the need to preserve human causal agency in human-AI mergers.
Kelley, C. et al. (2025). Special issue on equity of artificial intelligence in higher education. Journal of Computing in Higher Education. → Emphasizes equity as a guiding principle for human-AI partnerships.
Kray, L. J. et al. (2025). Psychological drivers of gender disparities. [Journal.] → Explores how growth mindsets and prestige-based status reduce workplace bias.
Leahy, S. et al. (2024). The Compendium. [Report.] → A guide to understanding and addressing extinction risks from advanced AI.
Lein, L. et al. (2026). Mapping Poverty Challenges to AI-driven Solutions. University of Michigan AI Laboratory. → Examines how AI solves poverty by involving affected communities in design.
Liu, A. et al. (2025). AI as a Teaching Partner: Early Lessons from Classroom Codesign. arXiv. → Demonstrates how AI can support teachers without replacing their professional judgment.
Malta, M. (2025). The quiet architecture of violence: Why LGBTQ+ health demands community ownership. Frontiers Science News. → Argues marginalized communities must own their data to prevent institutional harm.
Miotti, A. et al. (2025). A Narrow Path: How to secure our future. narrowpath.co. → Proposes a 20-year regulatory plan to ensure AI remains under human control.
Norström, A., et al. (2024). Resilience Science Must-Knows. Stockholm Resilience Centre, Global Resilience Partnership, Future Earth. https://doi.org/10.5281/ZENODO.17466370
Nichols, N., Nevo, S., & Greaves, M. (2025). Achieving a Secure AI Agent Ecosystem. RAND. → Recommends a security-first design for building trust between interacting AI agents.
Peters, J., & Rosén, M. (2025). Pain and Pleasure in working with sustainability. [Journal.] → Explores how educators manage emotions while advocating for societal transformation.
Sepulveda, A. (2011). Information-theoretic Metamodel of Organizational Evolution. (Doctoral dissertation). Walden University. → Uses information theory to model organizations as adaptive, self-similar networks.
Wilson, D. S., & Snower, D. J. (2024). Rethinking the Theoretical Foundation of Economics I: The Multilevel Paradigm. Economics, 18(1). → Argues that altruistic groups outperform selfish ones through multilevel selection.
Zaidi, S., & Johar, I. (2024). Position Paper for the Planetary Civics Inquiry. [Report.] → Advocates for governance frameworks based on collective intelligence and care.
Zhong, J. et al. (2022). Quantifying the Selective, Stochastic, and Complementary Drivers of Online Communities. [Journal.] → Measures how online community rules evolve through selection and imitation.
Resonance Science and Consciousness Foundations
These sources provide the biophysical and cosmological grounding for the resonance metaphor central to the framework — the claim that kindness and coherence are not merely social values but reflect fundamental patterns in how living systems maintain integrity across scales. This literature is unusual in AI literacy contexts and represents a distinctive theoretical contribution of the TAI-KPI framework.
Meijer, D. K. F., & Bermanseder, T. (2025). Novel Horizons of the Mirror Universe. [Journal.] → Investigates the sonic origins of gravity and dark energy as resonance phenomena.
Meijer, D. K. F., & Forghani, M. (2025). The Sonic and Conscious Universe. [Journal.] → Explores how wave resonance creates coherence across all scales of the universe.
Meijer, D. K. F., & Ivaldi, A. (2025). The Intelligence of the Cosmos. [Journal.] → Reveals a cosmic intelligence framework operating alongside human-created AI.
Meijer, D. K. F., Jerman, I., Melkikh, A. V., & Sbitnev, V. I. (2020). Consciousness in the Universe is Tuned by a Musical Master Code. Quantum Biosystems, 11(1), 1–31. → Proposes a fundamental frequency code that guides the structure of reality and living systems.
A note on this bibliography
These 50 sources were selected for their relevance across the interdisciplinary synthesis that the TAI-KPI framework represents. They span neuroscience, evolutionary biology, resilience and complexity science, information theory, educational equity, AI governance, and consciousness research — fields that are rarely brought into conversation with each other in AI literacy contexts.
The selection prioritizes publicly available sources where possible. For sources that require institutional access, the annotations are written to communicate the key contribution without requiring the reader to access the full text.
This bibliography will be updated as the framework is tested in educational and organizational contexts and as the research landscape develops.
For the full theoretical discussion of how these sources connect to the Bridge Checklist panels, see Theoretical Foundations.
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