TVP: Digital Child

A Living Lexicon

Digital Child is a growing lexicon of the ideas, people, policies, and technologies at the heart of Professor Oerther’s work — where technology and care intersect, evolve, and challenge one another. Each entry is a node; its cross-references are the edges. Read it here, or open it in NotebookLM to ask questions across the whole graph.

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The lexicon

Accountability

AI as Sociotechnical Mediator

AI Fluency

AI Literacy

AI Policy

Artificial Intelligence

CESB Statement on Artificial Intelligence

DIKW Hierarchy

NCEES Position on Responsible Use of Artificial Intelligence

Transparency

Verifiability

Accountability

Concept · Core entry · TAV framework

Accountability acknowledges that AI is – at best – more appropriately described as ‘augmented intelligence’ rather than ‘artificial intelligence’. In other words, until such time as the public comes to trust machines at a level similar to licensed human beings – subject to personal liability, litigation, and even incarceration – then the role of machines is to support – not to replace – humanity. Thus, no matter how AI is used, the human retains and accepts full responsibility both for the use as well as for the outcome.
Oerther, S., Oerther, D.B. (2026). The importance of transparency, accountability, and verifiability when using artificial intelligence in nursing education. Nurse Education in Practice 90:104557.

The practitioner-responsibility element of the TAV framework: the human user of AI retains and accepts full responsibility for both the use and the outcome, grounded in licensure’s commitment of personal liability to the licensed practitioner. The “augmented intelligence” framing names AI as a tool that supports – not replaces – the practitioner; the licensure-grounded argument is that machines cannot be held accountable because they are not subject to the personal liability, litigation, and incarceration that bind licensed humans. Institutional instantiation at the certification-board level is the Accountability principle of the CESB Statement on Artificial Intelligence.

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AI as Sociotechnical Mediator

Concept · Core entry

AI should be treated as a sociotechnical system that reshapes how knowledge is produced, shared, and validated (Oerther and Oerther, 2026c). […] AI is not a calculator. Like Zoom, AI mediates the relationship of sharing DIKW among people.
Oerther, Oerther, and Gadhamshetty (2026). Artificial Intelligence Is Not a Calculator: A Sociotechnical Mediator of Knowledge and Action for Engineers, Scientists, and Health Professionals. Environmental Engineering Science 43(6):195-198.

AI as digital infrastructure mediating how data, information, knowledge, and wisdom flow among people, not a stand-alone tool acting on data. Distinguishes AI from prior framings (AI-as-calculator, AI-as-tool) by locating its effect in the relational layer of professional practice.

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AI Fluency

Concept · Core entry

[D]igital fluency [is] the ability to adapt, evaluate, apply, and share knowledge with others.
Slagg (2025), as paraphrased in Oerther, Oerther, and Gadhamshetty (2026). Artificial Intelligence Is Not a Calculator: A Sociotechnical Mediator of Knowledge and Action for Engineers, Scientists, and Health Professionals. Environmental Engineering Science 43(6):195-198.

The practitioner’s operational capacity with AI – the ability to adapt, evaluate, apply, and share knowledge in working with AI. Instantiated institutionally in The Ohio State University’s AI Fluency Initiative and Purdue University’s “AI working competency” graduation requirement.

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AI Literacy

Concept · Core entry

AI Literacy is the practitioner capacity to operate within AI as a sociotechnical system that mediates the flow of data, information, knowledge, and wisdom among professionals and the public they protect. It is constituted by transparency, accountability, and verifiability as definitional requirements – not policy recommendations – and presupposes the personal accountability of the practitioner that no institutional process can hold for them.
D.B. Oerther, author.

The practitioner-side capacity term in the lexicon’s AI vocabulary. Distinguished from AI competency or AI fluency (skill in using AI tools) by its constitutive structure: AI Literacy is not a level of skill but a set of practices the practitioner exercises whenever AI is in use – disclosure of use, retention of responsibility, verification against primary sources.

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AI Policy

Concept · Core entry

AI Policy is the formal position by which a profession’s governing body sets the terms of acceptable artificial-intelligence use in professional practice.
D.B. Oerther, author.

The ratification of practitioner-level obligations for AI use into rules such as for a certification board, learned society, licensing board, or professional society. Instantiated by the CESB Statement on Artificial Intelligence (Accountability, Transparency, Ethics) and the NCEES Position on Responsible Use of Artificial Intelligence (a five-element position). Distinct from the TAV framework that supplies those obligations: TAV obligates the individual practitioner; AI Policy obligates an institution’s members through its governing instrument.

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Artificial Intelligence

Technology · Core entry

Artificial Intelligence refers to computational systems – including but not limited to widely available large language models – that produce outputs resembling human cognitive work. AI is neither a calculator nor a stand-alone tool; its professional significance lies in how it mediates the flow of data, information, knowledge, and wisdom among practitioners and the publics they serve.
D.B. Oerther, author.

The term originates in the 1955 proposal for the Dartmouth Summer Research Project, held in 1956, that named the field; this entry takes the narrower computation-and-cognitive-output sense the constellation uses, not the founding agenda’s broader reach into perception, planning, and robotics. The lexicon’s substantive claims about AI live in the entries that take up its character (AI as Sociotechnical Mediator), its operation (DIKW Hierarchy), its governance (TAV), and its institutional reception (CESB Statement on Artificial Intelligence, NCEES Position on Responsible Use of Artificial Intelligence).

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CESB Statement on Artificial Intelligence

Policy · Core entry

Artificial Intelligence, including but not limited to widely available large language models, may be used in the activities of CESB while adhering to three principles: Accountability: CESB requires human accountability when using AI (i.e., human oversight responsibility remains with the individual using the AI tool). Transparency: CESB requires disclosure when using AI (i.e., at a minimum state that ‘AI was used’; preferred approaches may include identifying the platform and version used and, where appropriate, describing the approach or prompts used). Ethics: CESB requires ethical practice when using AI (i.e., any use of AI is subject to the same rigorous ethics required of any professional activity, including but not limited to current best practices in confidentiality and data protection as well as verification of data and outputs).
Council of Engineering and Scientific Specialty Boards (2026). CESB Statement on Artificial Intelligence, March 10, 2026.

The three-principle AI policy promulgated under D.B. Oerther’s CESB presidency: Accountability (human oversight remains with the individual), Transparency (disclose use), and Ethics (rigorous ethical practice, confidentiality, data protection, verification). Institutional instantiation of the TAV framework at the certification-board level, substituting Ethics for Verifiability.

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DIKW Hierarchy

Concept · Core entry

AI does not merely calculate values; it facilitates the transition from raw environmental or clinical data to actionable professional wisdom, such as identifying the need for stream reaeration or medical intervention.
Oerther, Oerther, and Gadhamshetty (2026). Artificial Intelligence Is Not a Calculator: A Sociotechnical Mediator of Knowledge and Action for Engineers, Scientists, and Health Professionals. Environmental Engineering Science 43(6):195-198, p. 196.

The four-stage progression from data (raw observations) through information (contextualized facts) and integrated knowledge (understanding) to wisdom (actionable, accountable judgment, the element a system of caring installs in the practitioner). The progression is information science’s data-information-knowledge-wisdom hierarchy, its consolidation commonly credited to Ackoff (1989); the constellation inherits the ladder and gives its terminus the content the lexicon defines at Wisdom. The wisdom terminus measures the extent of the attitude (A) component of practice. The disposition is held tacitly, below conscious deliberation; what is measured, and what the practitioner is accountable for, is the judgment it issues in when exercised within a system of caring. The stream-reaeration and medical-intervention examples in the source illustrate the endpoint of the progression – wisdom applied to environmental engineering and nursing practice respectively.

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NCEES Position on Responsible Use of Artificial Intelligence

Policy · Core entry

Artificial intelligence (AI) is a transformative tool for engineering and surveying practice, and NCEES affirms that its use must prioritize public safety, professional responsibility, and ethical standards. While AI-powered tools can automate complex tasks, human interaction and oversight remains essential to ensure proper use, accuracy, reliability, and adherence to industry standards. Engineers and surveyors must maintain competence in their practice, understanding both AI’s capabilities and its limitations to make informed judgments. Additionally, ethical considerations, including data privacy, bias mitigation, and accountability, should be prioritized to uphold public trust and professional integrity.
National Council of Examiners for Engineering and Surveying (2025). Manual of Policy and Position Statements, Position Statement 6.10: Responsible Use of Artificial Intelligence in Engineering and Surveying. Greenville, SC: NCEES.

The 2025 NCEES position on AI in engineering and surveying practice, organized in five elements: Responsible Charge, Competence, Validation/Transparency, Ethical Considerations, and Guidance and Collaboration.

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Transparency

Concept · Core entry · TAV framework

Transparency acknowledges that AI is a rapidly evolving technology, and that the human users of AI have an obligation to share with other humans the details of the use of AI. […] [T]ransparency – including both disclosure as well as details suitable for replication – must be paramount in the current, ever-evolving field of AI in nursing education research and publication.
Oerther, S., Oerther, D.B. (2026). The importance of transparency, accountability, and verifiability when using artificial intelligence in nursing education. Nurse Education in Practice 90:104557.

The disclosure element of the TAV framework: the human user of AI has an obligation to share with other humans the details of the use of AI, including disclosure and details suitable for replication. Operationalized in nursing-education research through the Contributor Roles Taxonomy (CRediT). Institutional instantiation at the certification-board level is the Transparency principle of the CESB Statement on Artificial Intelligence, which sets the operational floor at “AI was used” and the preferred ceiling at platform, version, and prompt disclosure.

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Verifiability

Concept · Core entry · TAV framework

Verifiability includes both the conceptual understanding as well as the practical understanding of AI; in other words, the nursing educator must help to open the ‘black box’ of AI. Concerns such as bias and hallucination are part of verifiability. Similarly, the ethical issues associated with intellectual property are part of verifiability.
Oerther, S., Oerther, D.B. (2026). The importance of transparency, accountability, and verifiability when using artificial intelligence in nursing education. Nurse Education in Practice 90:104557.

The black-box-opening element of the TAV framework: the practitioner must hold conceptual and practical understanding of AI – including bias, hallucination, and the intellectual-property issues associated with AI training and authorship. Operationalized through peer review and Evidence-Based Practice as established mechanisms for evaluating outputs against primary sources. In the CESB Statement on Artificial Intelligence, the institutional instantiation substitutes the broader Ethics principle for Verifiability, absorbing verification of data and outputs into a wider ethical-practice frame.

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The lexicon is updated continuously. Each node represents a concept, person, policy, or technology — and every edge tells a story.