About ALICE
ALICE — Adaptive Learning Intelligence via Cognitive Engines — is a thinking platform: an environment built on the premise that knowledge is a landscape to be explored, that every learner's path through it is genuinely their own, and that the technology mediating that exploration does not merely transmit knowledge but actively acts on it.
That premise was developed over three decades of theoretical and applied work at the intersection of mathematics, engineering, literary theory, and the study of hypertext as a communicative medium -- long before the edtech industry had a framework for it.
Theoretical Foundations
ALICE is built on three intellectual pillars that are structural, not decorative.
Roland Barthes' concept of the lexia -- the atomic unit of a text whose meaning is relational rather than self-contained -- defines the basic unit of knowledge in ALICE. A lesson in ALICE is not a module to be completed; it is a lexia whose significance shifts depending on which other lexias the learner has encountered and in what order.
Gilles Deleuze and Félix Guattari's rhizome model defines the architecture of the knowledge graph. There is no root, no trunk, no prescribed hierarchy. Every concept is a potential entry point. Every connection is traversable in both directions. The path a learner follows is genuinely their own because the platform was designed to have no single correct path.
The third pillar is a theoretical claim articulated by Prof. Juan B. Gutiérrez in 2000, before the technology existed to fully realize it: that the medium of communication does not merely carry the message -- it acts on it. In the context of adaptive learning with AI agents, today it is a precise technical description of what AI agents actually do. The agents in ALICE do not retrieve or summarize content. They engage with it as active participants in the communicative event, producing meaning in the encounter rather than transmitting it from a repository.
A Thirty-Year Genealogy
The intellectual lineage of ALICE begins with Literatronica, a literary hypertext engine developed in the mid-1990s by Prof. Juan B. Gutiérrez and funded by cultural institutions in Colombia and Spain. Literatronica was not an educational platform -- it was a working literary system that demonstrated the viability of rhizomatic navigation as an authoring and reading architecture.
That work attracted serious scholarly attention. Nine doctoral dissertations, three master's theses, and five peer-reviewed articles by researchers in Spain, Germany, the United States, Iceland, Colombia, and Argentina examined Literatronica's theoretical and technical contributions. That body of secondary literature existed before ALICE had its first grant.
When the first NSF award arrived, the architecture was extended into education. The platform became ALICE: Adaptive Learning for Interdisciplinary Collaborative Environments. The lexia and the rhizome remained, now serving the construction and navigation of course knowledge graphs.
The current generation adds a third architectural layer that the original theory anticipated but could not yet build: cognitive agents that are themselves active communicative media. The platform is now ALICE: Adaptive Learning Intelligence via Cognitive Engines.
Architecture
ALICE is composed of three commercially separable components:
ALICE is the knowledge platform itself -- the content graph, the lexia model, the rhizomatic navigation engine, and the multilingual content layer. It can operate independently as an adaptive content environment.
MAX (Multi-Agent Consensus System) is the orchestration layer. It manages ensembles of AI agents that engage with content, critique responses, and reach consensus through a formal protocol designed to resist the failure modes of single-model reasoning.
HUDO (Human Oversight and Audit Layer) is the cryptographic commitment chain. Every consequential interaction is timestamped and signed, producing an independently verifiable audit trail. HUDO exists because accountability in AI-assisted learning is not optional.
Current Deployment
ALICE is currently deployed in DAIR-3, a research training program at the University of Texas at San Antonio for faculty, postdoctoral researchers, and doctoral students working in data-intensive science. The platform serves professionals who evaluate intellectual claims critically -- the same audience for which it was designed.
Funding
The development of ALICE has been sustained across three decades by funding agencies on three continents. Peer review at this scale and over this duration is not a credential. It is a record.
USA: National Science Foundation (NSF). Award #2518973, 2026-2029.
USA: National Institute of General Medical Sciences (NIH NIGMS). Award #1R25GM151182, 2023-2028.
USA: National Science Foundation (NSF). Award #1645325, 2016-2019.
Spain: Ministerio de Industria y Turismo de España. Sub-award from contract TSI-070300-2008-67, 2008-2009.
Colombia: Instituto Distrital de Cultura y Turismo de Bogotá. Award #IDCT-410/1998.
Colombia: Instituto Distrital de Cultura y Turismo de Bogotá. Award #IDCT-514/1997.
Colombia: Colcultura -- Instituto Colombiano de Cultura, now the Ministerio Colombiano de Cultura. Award #COLCULTURA-SECAB 014/1996.