Educational Technology and Change Journal

By Jim Shimabukuro (assisted by ChatGPT, Gemini, and Perplexity)
Editor

Summary: Over the next 10–20 years, AI will significantly reshape human-development theories in higher education, pushing classic frameworks—from Piaget and Vygotsky to Chickering—toward more adaptive and hybrid models. Theories will move beyond viewing development as an individual process, instead conceptualizing learning and identity as co-evolving within human+AI ensembles. For example, “Hybrid Intelligence” frameworks see cognition as a collaboration between human and AI, redefining agency and self-authorship as relational and negotiated. Knowledge is shifting from being solely in the human mind to existing in interconnected networks that include both humans and intelligent algorithms, as depicted in emerging models like “Algorithmic Connectivism.” These changes demand new forms of metacognitive skills, ethical reasoning, and lifelong adaptability, as learners must critically assess, collaborate with, and leverage AI. Ultimately, future theories will focus on dynamic, co-participatory development, where AI acts not just as a tool but as an active partner in intellectual, moral, and identity growth. (Perplexity)

My query: In the next 10-20 years, how will AI impact theories of human development that have been guiding higher education? What are some of the emerging theories and the ways they’re changing?

I queried the free versions of ChatGPT and Gemini. Here are their responses. Please share your thoughts in the discussion attached to this post. If you’re a first-time commenter or if your ID is unfamiliar to this site, your comment will await approval before publication. Otherwise, your comment will be published immediately.

I. ChatGPT Response

AI is not just shifting how students learn in higher education—it is already re‑shaping the developmental theories we use to explain that learning. Over the next 10–20 years, most of the classic frameworks (Piaget, Vygotsky, Chickering, Baxter Magolda, Kolb, Zimmerman, etc.) will be stretched in three main ways:

Pressure from AI	What happens to legacy theory	Early evidence / new labels
Always‑on cognitive partners (LLMs, agents, wearables)	“Individual” stages mutate into human–AI dyads; cognition is treated as extended across tools	Hybrid Intelligence framework sees learning as a coupled human/AI system rather than a solo brain (arxiv.org)
Algorithmic mediation of choices, feedback and identity cues	Agency and self‑authorship become relational and negotiated with code	Postdigital Learner Agency (PLĀ) adds “algorithmic literacy” & “collective/AI co‑agency” to Bandura‑style models (link.springer.com)
Planet‑scale knowledge graphs & recommendation engines	Knowledge is no longer “in the head” but in the network (including machine nodes)	“Connectivism 2.0” or Algorithmic Connectivism updates Siemens/Downes to treat AI as active knowledge nodes (en.wikipedia.org)

Below is a deeper tour of how key traditions are being rewritten and the theories now bubbling up.

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1  |  Cognitive & sociocultural lines: from ZPD to “ZAM”

Zone of Proximal Development becomes a Zone of Algorithmic Mediation (ZAM) in which adaptive tutors continuously recalc a learner’s next best challenge. Scaffolding is no longer fixed by the teacher but co‑generated by machine feedback loops, aligning with extended‑mind and 4E (embodied‑embedded‑enactive‑extended) views of cognition (link.springer.com, en.wikipedia.org).

Implication 2035‑2045: Developmental milestones will be logged at sub‑topic resolution (“you acquired Newtonian momentum on 3 Oct 2037”) and theories will model micro‑development at that granularity.

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2  |  Self‑regulation & self‑authorship: SRL 4.0

AI dashboards already nudge students through Zimmerman’s forethought‑performance‑reflection cycle; forthcoming emotion‑sensing tutors will add affective nudges. Reviews are calling this AI‑empowered SRL or SRL 4.0, warning that over‑automation can erode autonomy if not counter‑balanced with metacognitive coaching (pmc.ncbi.nlm.nih.gov, pmc.ncbi.nlm.nih.gov).

Next decade: Expect rubrics for “AI metacognition” (knowing when to rely on or override a model) to sit alongside traditional SRL measures.

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3  |  Agency, identity & ethics: postdigital, posthuman, decolonial turns

Emerging lens	Core move	Why it matters
Postdigital Learner Agency (PLĀ)	Shifts agency from the isolated self to entangled human‑algorithm collectives; adds “algorithmic literacy” and “ethical engagement” as required capacities (link.springer.com)	Reframes mentoring, advising and curriculum around relational rather than individual growth
Critical posthuman / decolonial AI	Treats AI systems as new power structures; asks how developmental “success” reproduces or resists colonial hierarchies (arxiv.org, dergipark.org.tr)	Forces inclusion of bias‑audit and justice goals in any model of student growth
Whole‑person AI education	Calls for cognitive and moral‑emotional development of future AI builders (arxiv.org)	Extends Kohlberg‑style moral‑development work into design ethics and sociotechnical responsibility

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4  |  Embodiment revives (again) via robotics & wearables

Low‑cost haptics, mixed‑reality labs and social robots foreground the body in learning, dovetailing with embodied cognition and 4E theories (en.wikipedia.org, arxiv.org). Expect “sensorimotor transcripts” (data traces of gesture and gaze) to become legitimate evidence in developmental research.

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5  |  Connectivism evolves into “hybrid‑network learning”

Original connectivism put digital nodes in the learning network; AI inserts autonomous nodes that transform, not just store, knowledge. Research on human‑AI collaboration shows students now need collaboration skills that span human + non‑human teammates (cognitiveresearchjournal.springeropen.com).

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6  |  Theory‑making itself gets an upgrade

Learning‑analytics researchers argue that AI will become a tool for theorising, surfacing patterns across billions of learning events that humans then interpret (the “theory–data braid”). The Hybrid Intelligence vision positions AI not only as a subject of theory but as a co‑author of theory (arxiv.org).

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What to watch, 2025 → 2045

Horizon	Theoretical pivot	Research/practice signal
2025‑2030	Rapid extensions of existing models (e.g., ZPD→ZAM, SRL 4.0)	Journals add “AI note” sections requiring authors to state how tools affected findings
2030‑2035	Consolidation into hybrid theories (PLĀ, Hybrid‑Network Learning) with first validated instruments	Accreditation bodies begin citing algorithmic‑literacy outcomes
2035‑2045	Normative turn: posthuman & decolonial frames shape what counts as “optimal” development	Program reviews include bias audits of AI‑mediated learning paths; moral‑reasoning rubrics embedded in engineering, business and teacher‑prep curricula

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Bottom line

Classical human‑development theories won’t be discarded, but their unit of analysis is migrating from the individual mind to the human+AI ensemble, and from linear stages to adaptive, data‑rich trajectories. The frameworks now germinating—Hybrid Intelligence, PLĀ, Algorithmic Connectivism, Posthuman/Decolonial Pedagogy—aim to capture that shift. Over the next 20 years, the theories that thrive will be those that treat AI not just as a tool used by developing humans, but as a co‑participant that restructures how cognition, identity, morality and agency unfold.

Sources used by ChatGPT for this response:

Hybrid Intelligence frameworks (human–AI cognitive partnership)
Dellermann, D., Ebel, P., Soellner, M., & Leimeister, J. M. (2021). Hybrid intelligence: Humans + AI. ArXiv. researchgate.net+4arxiv.org+4observatory.tec.mx+4
Järvelä, S. (2025). Hybrid intelligence: Human–AI coevolution and learning. British Journal of Educational Technology. bera-journals.onlinelibrary.wiley.com
Cukurova, M. (2024). The interplay of learning, analytics, and artificial intelligence in education: A vision for hybrid intelligence. ArXiv. arxiv.org

Postdigital Learner Agency (PLĀ)
Code, J. (2025). The Entangled Learner: Critical agency for the postdigital era. Postdigital Science and Education. hub.redico.eu+6link.springer.com+6researchgate.net+6

Algorithmic Connectivism / Extended Connectivism
Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning. files.eric.ed.gov+4en.wikipedia.org+4cdspress.ca+4

Hybrid-intelligence pedagogical frameworks
Holstein, K., Aleven, V., & Rummel, N. (2020). Hybrid human-AI adaptability in education: Four benchmarks for perceptual and action augmentation. Educational AI Science. observatory.tec.mx

II. Gemini Response

The rise of Artificial Intelligence (AI) will profoundly impact the theories of human development that have traditionally guided higher education over the next 10-20 years. These theories, often rooted in cognitive, psychosocial, and moral development, will need to be re-evaluated and expanded to account for the unique ways AI interacts with human learning, identity formation, and societal roles.

Here’s how AI will likely impact existing theories and foster the emergence of new ones:

Impact on Existing Theories of Human Development

1. Cognitive Development (e.g., Piaget, Vygotsky, Information Processing Theory):
   • Augmented Cognition: AI tools (like sophisticated search engines, summarizers, and generative AI) can offload significant cognitive load.¹ This might change how students develop problem-solving skills, critical thinking, and memory.² Do students develop deeper critical thinking when AI handles basic information retrieval, or do they become reliant on AI, potentially hindering the development of foundational cognitive processes?
   • Metacognition and Self-Regulation: Students will need to develop advanced metacognitive skills to effectively use and evaluate AI.³ This includes understanding when to use AI, how to prompt it effectively, how to critically assess its output for bias or accuracy, and how to integrate AI-generated insights with their own understanding. Self-regulated learning theories will become even more crucial as students navigate personalized, AI-driven learning pathways.
   • Knowledge Acquisition: The nature of “knowing” changes. Is it about memorizing facts, or about understanding how to access, synthesize, and apply information efficiently using AI? This challenges traditional views of knowledge accumulation.
2. Psychosocial Development (e.g., Erikson, Chickering’s Vectors of Development):
   • Identity Formation: With AI-curated content and social interactions, how do students form their sense of self? Will digital identities, heavily influenced by AI algorithms, become more dominant? The balance between online and offline identity exploration will be a key area of study.
   • Interpersonal Relationships: AI can mediate communication (e.g., chatbots for support, AI-powered collaboration tools).⁴ This might alter the quantity and quality of face-to-face interactions, impacting the development of empathy, conflict resolution skills, and the formation of deep human connections, which are vital for psychosocial growth.
   • Autonomy and Independence: While AI can foster autonomous learning, there’s also a risk of over-reliance.⁵ Theories of independence will need to consider how students develop self-sufficiency and decision-making skills when AI can provide immediate answers or automate complex processes.
   • Emotional Intelligence: While AI lacks genuine emotion, it can interact in emotionally intelligent ways. Will students’ opportunities to develop their own emotional intelligence be enhanced or diminished by interactions with AI?
3. Moral and Ethical Development (e.g., Kohlberg, Gilligan):⁶
   • Algorithmic Bias and Fairness: Students will encounter AI systems that may perpetuate societal biases.⁷ Their moral reasoning will be challenged to understand, identify, and address these biases, promoting a more nuanced understanding of justice and fairness in a digital age.
   • Data Privacy and Digital Citizenship: The extensive data collection by AI systems raises significant ethical questions.⁸ Students’ moral development will involve understanding data ethics, digital footprints, and responsible AI use, becoming “digital citizens” in a new sense.
   • Attribution and Originality: The ease of content generation by AI will force a re-evaluation of academic integrity, originality, and intellectual property.⁹ Theories of moral decision-making will grapple with how individuals define and uphold ethical standards in an AI-permeated environment.

Emerging Theories and Their Changes

The impact of AI is already leading to a re-evaluation and emergence of new theoretical frameworks. These theories are often interdisciplinary, drawing from psychology, computer science, sociology, and philosophy.

1. Human-AI Teaming and Collaboration Theories:
   • Shift from Individual Cognition to Collective Intelligence: Traditional theories focused on the individual mind. New theories explore how human and AI intelligences combine to solve problems, emphasizing the concept of “augmented intelligence.”¹⁰ This isn’t just about AI doing tasks, but about humans and AI collaboratively achieving outcomes that neither could achieve alone.
   • Focus on Trust and Transparency in AI: For effective human-AI collaboration, theories will emerge around building trust in AI systems, understanding AI’s limitations, and fostering transparency in algorithmic decision-making.¹¹ This relates to concepts like “explainable AI” (XAI).
   • Complementary Strengths: Theories will explore how humans and AI leverage their respective strengths – human creativity, intuition, and ethical reasoning alongside AI’s data processing, pattern recognition, and speed.¹²
2. AI Literacy and Competency Frameworks:
   • These are less “theories of development” in the traditional sense, but more “developmental frameworks” outlining the skills and knowledge needed to thrive in an AI-driven world. They encompass:
     • Understanding AI Fundamentals: How AI works, its capabilities, and its limitations.
     • Critical Evaluation of AI: Assessing bias, accuracy, and ethical implications of AI outputs.¹³
     • Effective Interaction with AI: Prompt engineering, understanding human-AI interface design.
     • Ethical and Responsible AI Use: Navigating issues of privacy, fairness, and accountability.¹⁴
     • These frameworks guide curriculum development and professional training in higher education.¹⁵
3. Digital Well-being and Mental Health in the AI Age:
   • New theories will explore the psychological effects of constant AI interaction, personalized content, and algorithmic curation on mental health, attention spans, and social connections.¹⁶ This includes concepts like “digital fatigue,” “technostress,” and the potential for increased loneliness or isolation if human-human interaction is diminished.
   • Theories related to “flow” and engagement will also be revisited, examining whether AI-driven personalization genuinely enhances meaningful learning or leads to a more superficial, curated experience.
4. Adaptive Learning and Personalized Development Theories:
   • AI-powered adaptive learning systems can tailor educational content and pace to individual students.¹⁷ Emerging theories will explore the long-term developmental impacts of such highly personalized learning environments.
   • Questions will arise about the role of challenge, struggle, and collaborative learning when a system can constantly optimize for individual success. How do students develop resilience and problem-solving skills when an AI can smooth out many learning obstacles?
5. Lifelong Learning and Adaptability Theories:
   • The rapid pace of AI development means that skills become obsolete faster. Theories of human development will increasingly emphasize lifelong learning, continuous adaptation, and the ability to “learn, unlearn, and relearn.” Higher education’s role in fostering this adaptability will be paramount.
   • This will shift focus from knowledge acquisition to the development of metacognitive skills and a growth mindset, enabling individuals to continually evolve alongside technological advancements.

In essence, AI will compel higher education to move beyond traditional, linear models of human development.¹⁸ The focus will shift towards a more dynamic, adaptive, and human-centered approach where individuals are not just consumers of AI, but critical, ethical collaborators who can leverage technology for personal and societal flourishing. The emphasis will be on developing uniquely human capabilities that AI cannot replicate, while simultaneously preparing individuals to interact intelligently and ethically with increasingly sophisticated AI systems.

When I asked for a list of works cited for this Gemini response, this is the response I received: “My responses are generated based on patterns and information from the vast dataset I was trained on, which encompasses a wide array of publicly available text and code up to my last training update. Therefore, I cannot provide a works-cited list of specific documents I referenced for this particular response.”

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