By Cristo Leon, Ph.D.
Last reviewed September 30th, 2026.
Higher education still commonly describes instruction through categories such as face-to-face, online, hybrid, synchronous, asynchronous, and HyFlex. These classifications remain useful for administration, scheduling, and course organization, but they increasingly provide an incomplete account of how learning actually occurs.
In a new book chapter co-authored with Cristo León, “Beyond Online and Face-to-Face: Converged Learning, Generative AI, and Strategic Planning in the Digital Era,” we revisit the concept of Converged Learning and ask what happens when generative artificial intelligence becomes part of the learning environment itself. The chapter appears in Analytical Methodologies in Educational Sciences, published by Brazilian Journals Editora. Beyond Online and Face-to-Face
Our central argument is that GenAI is not simply another technology added to the classroom. It changes the conditions under which intellectual work is produced.
From Delivery Modes to Conditions of Learning
The original Converged Learning framework challenged the assumption that physical and online education should be understood as separate instructional environments.
Instead, learning can occur across a continuum of time, place, access, and participation. A learner may participate physically, connect remotely in real time, work asynchronously, or move among these configurations as circumstances change. The important shift is from assigning the learner to a fixed instructional mode toward designing an environment within which multiple forms of participation are possible. Beyond Online and Face-to-Face
Generative AI extends this argument considerably.
Earlier digital technologies primarily changed access. Converged Learning expanded the possibilities for participation. GenAI increasingly affects cognition because artificial systems can participate in activities such as interpretation, synthesis, formulation, critique, revision, and production. Beyond Online and Face-to-Face
This suggests a progression:
Access → Participation → Cognition
The question is therefore no longer simply:
Where is the learner, and when is the learner participating?
We must increasingly ask:
Who or what is performing the intellectual work, what is being delegated, and who remains responsible for the resulting knowledge claims?
Intellectual Delegation Changes the Learning Environment
A videoconferencing platform enables communication. A learning management system organizes resources. A search engine retrieves information.
A generative AI system can do something conceptually different. It may explain a concept, summarize evidence, generate examples, critique an argument, translate ideas between domains, construct a draft, or revise an existing response.
We describe the intentional transfer of some intellectual operations to another person or computational system as intellectual delegation. Beyond Online and Face-to-Face
Delegation itself is not necessarily problematic. People have always delegated intellectual work through collaboration, specialization, editing, mentoring, consultation, and the use of computational tools.
The strategic and educational questions concern:
«What is delegated, why it is delegated, how the resulting output is evaluated, and who retains responsibility for the final intellectual product» – Cristo Leon, 2026
That distinction becomes particularly important for assessment.
Presence Is Not Cognition
One of the consequences of GenAI is that familiar indicators of learning become less transparent.
Physical presence is not necessarily participation.
Participation is not necessarily engagement.
Engagement is not necessarily cognition.
And the production of an artifact is no longer necessarily evidence of the cognitive activity that an instructor intended to assess. Beyond Online and Face-to-Face
This leads us to the concept of cognitive integrity: the degree to which a learner or professional retains accountable participation in the intellectual operations that are central to a task, even when collaborators or technologies are involved.
Cognitive integrity does not mean that every activity must be completed without assistance. Instead, the individual must understand what has been delegated, possess enough foundational knowledge to evaluate the results, and remain responsible for the reasoning and claims ultimately presented. Beyond Online and Face-to-Face
From Learner Agency to Epistemic Agency
Converged Learning initially emphasized learner agency in relation to participation: students could exercise greater control over where and how they learned.
The GenAI environment requires a broader conception of agency.
We distinguish between collaborative agency and epistemic agency.
Collaborative agency concerns the capacity of learners, instructors, peers, mentors, and technological systems to coordinate activity toward a shared objective.
Epistemic agency concerns something more specific: the learner’s capacity to make, justify, and remain accountable for decisions about knowledge—what to investigate, what to delegate, what to verify, what to reject, and ultimately what to claim. Beyond Online and Face-to-Face
An individual may therefore become highly proficient at using AI without necessarily possessing strong epistemic agency.
Prompting proficiency alone is not enough.
A learner also needs disciplinary knowledge, verification skills, judgment regarding delegation, cognitive integrity, and the capacity to determine whether an apparently persuasive answer should actually be accepted.
From Information Scarcity to Epistemic Abundance
Digital education was historically shaped in part by problems of access to information and expertise.
GenAI changes that condition.
Learners can now generate explanations, examples, interpretations, translations, cases, arguments, counterarguments, and representations almost instantaneously.
We describe this as epistemic abundance.
Importantly, epistemic abundance is not an abundance of verified knowledge. It is an abundance of accessible and rapidly generated knowledge claims and representations. Beyond Online and Face-to-Face
That distinction matters.
More answers do not necessarily produce better knowledge.
The educational challenge increasingly shifts from providing information toward developing the capacity to evaluate information: comparing claims, examining evidence, detecting errors or bias, reconciling conflicting accounts, and determining why one interpretation should be accepted over another.
A Multidimensional Converged Learning Ecosystem
These changes suggest that Converged Learning should no longer be represented only as a continuum between face-to-face and online learning.
In the chapter, we propose a multidimensional ecosystem organized around nine dimensions:
- Place: physical ↔ digital ↔ distributed
- Time: human synchrony ↔ machine synchrony ↔ asynchrony
- Participation: observing ↔ participating ↔ co-producing
- Agency: instructor ↔ learner ↔ peers ↔ AI
- Cognition: human ↔ AI-augmented ↔ AI-delegated
- Content: human-created ↔ AI-generated ↔ co-created
- Interaction: human-human ↔ human-AI ↔ AI-mediated human-human
- Assessment: process + provenance + product
- Reconfigurability: configurations can change as goals, evidence, technologies, policies, or constraints change. Beyond Online and Face-to-Face
The last dimension is especially important.
A contemporary learning environment is not merely hybrid. It can reconfigure while learning is taking place.
A student may move from independent reasoning to consultation with AI, from AI-supported analysis to peer collaboration, and then to instructor arbitration—all within one task.
Convergence therefore becomes a design principle rather than simply a delivery category. Beyond Online and Face-to-Face
Making Agency Visible
If intellectual activity is distributed across people and computational systems, educators also need appropriate ways of making that distribution visible.
This does not mean recording every interaction.
Instead, we propose visible markers of agency: proportionate evidence capable of showing where consequential intellectual work occurred.
Examples may include AI-use disclosures, annotated drafts, revision histories, selected prompt or interaction logs, version control, source-tracing notes, oral defenses, decision memos, and brief provenance statements. Beyond Online and Face-to-Face
The goal is not surveillance.
The goal is sufficient transparency to understand how intellectual work was produced and where responsibility resided.
Convergence Describes Change; Strategic Planning Organizes the Response
Recognizing that the environment has changed does not tell an institution what to do about it.
For this reason, the chapter connects Converged Learning with the Universal Strategic Planning model (USP), adapted from our earlier Modelo Universal de Planeación Estratégica (UPE).
The relationship between the frameworks is straightforward:
Convergence helps us understand what is changing.
Universal Strategic Planning helps us determine how to respond. Beyond Online and Face-to-Face
USP organizes strategy through four broad dimensions:
- Pre-planning — What is happening, and why does it matter?
- Strategic planning — What should we do, and what do we need?
- Implementation — What will we produce, and what change should occur?
- Monitoring — How will we know whether it worked, and how should we adjust? Beyond Online and Face-to-Face
This sequence provides an important corrective to much of the current conversation around artificial intelligence.
Organizations often begin by asking:
How can we use AI?
Strategic planning suggests a different starting point:
What problem are we trying to solve, for whom, under what conditions, with which resources, and with which combination of human and artificial capabilities?
Technology should follow strategic analysis rather than substitute for it.
AI Output Is Not a Strategy
This distinction becomes particularly important when organizations evaluate implementation.
Producing fifty AI-generated advertisements is an activity or output.
Generating impressions is a performance indicator.
Increasing qualified engagement may be an outcome.
Improving acquisition, retention, or customer experience may represent a strategic result.
The chapter therefore emphasizes a simple principle:
Clicks are not a strategy. Followers are not a strategy. AI-generated content is not a strategy.
The strategic question is what change those activities are intended to produce. Beyond Online and Face-to-Face
The same reasoning applies to education.
A polished AI-assisted assignment is an output. It does not, by itself, demonstrate that the intended learning process occurred.
Beyond Education: Converged Digital Ecosystems
Although the framework emerged from education, the same convergence can be observed in digital marketing and organizational communication.
A consumer may encounter a product through social media, search for additional information, consult an AI system, communicate with a human representative, visit a physical location, purchase through an e-commerce platform, interact with automated customer service, and later publish a review.
From the consumer’s perspective, these interactions may constitute one connected experience even though organizations manage them through separate departments and technologies. Beyond Online and Face-to-Face
The similarity with Converged Learning is significant.
Educational institutions classify learning according to delivery channels while learners increasingly experience interconnected learning ecosystems.
Organizations classify marketing according to channels while consumers increasingly experience interconnected customer ecosystems.
Strategy must therefore focus increasingly on experiences, problems, stakeholders, and desired outcomes—not technologies or channels in isolation.
Collaborative Orchestration
These environments also change the role of the instructor and manager.
The instructor increasingly coordinates learners, collaborators, platforms, artificial systems, rules, assessments, and varying forms of intellectual delegation.
We describe this function as collaborative orchestration:
the intentional arrangement and adjustment of people, tools, rules, interactions, and responsibilities so that distributed agency contributes to a coherent objective.
The instructor consequently becomes not less important but differently important—as mentor, coach, facilitator, guide, arbitrator, rule interpreter, and mediator of increasingly complex human-computational environments. Beyond Online and Face-to-Face
The same applies to management.
Managers increasingly orchestrate human teams, platforms, data systems, automation, generative tools, customer interactions, and feedback mechanisms.
Strategic competence therefore requires neither indiscriminate technological adoption nor technological avoidance. It requires judgment about which combinations of people, processes, and computational capabilities are appropriate for the objective being pursued.
The Question Is Not “What Can AI Do?”
The broader argument of the chapter can ultimately be reduced to a change in the strategic question.
Instead of asking:
What can we do with this technology?
We should ask:
What change are we trying to create, for whom, under what conditions, through which combination of human and computational capabilities, which intellectual operations should be delegated, who retains authority over consequential claims and decisions, and how will we know whether the intended change occurred?
The chapter concludes that the task of educators and managers is neither to eliminate technological mediation nor to maximize it. Their task is to orchestrate dynamic systems in which human judgment, collaboration, creativity, intellectual struggle, computational assistance, and responsibility are intentionally aligned with desired outcomes. Beyond Online and Face-to-Face
In that sense, Converged Learning is becoming something larger than a model of instructional delivery.
It provides a way of understanding reconfigurable socio-technical and socio-cognitive ecosystems in which agency itself becomes a design variable.
Publication
León, C., & Lipuma, J. M. (2026). Chapter 16 – Beyond Online and Face-to-Face: Converged Learning, Generative AI, and Strategic Planning in the Digital Era. In Analytical Methodologies in Educational Sciences (1st ed., pp. 282–311). Brazilian Journals Editora.
DOI: 10.35587/brj.ed.978-65-6016-163-4_16
The published chapter appears as Chapter 16, beginning on page 282 of the volume.