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Added section about alternatives

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Viswamedha Nalabotu 2026-03-11 14:51:26 +00:00
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@ -61,9 +61,11 @@ User & j.thompson@example.com & password \\
\end{tabular}
\end{center}
\textit{Note: I will try to keep the public website available, but the GPU node runs on my home PC and may occasionally go offline. For reliable testing, I recommend running the system locally on a machine with a GPU.}
\textit{Note: I will try to keep the public website available, but the GPU node
runs on my home PC and may occasionally go offline. For reliable testing,
I recommend running the system locally on a machine with a CUDA-enabled GPU.}
Manager registration code (if required): \texttt{MANAGER2026}
Manager registration code (for signup): \texttt{MANAGER2026}
\section{Introduction}\label{introduction}
@ -268,6 +270,26 @@ APIs, supports offline or air-gapped environments, and aligns with
enterprise privacy requirements while maintaining acceptable inference
performance.
\subsection{Positioning Against Alternative
Approaches}\label{positioning-against-alternative-approaches}
Dynavera was designed against three practical alternatives. First,
human-only onboarding preserves expert nuance but does not scale well
and introduces recurring opportunity cost for senior staff. Second,
static LMS/document-first onboarding scales distribution but provides
limited adaptivity, weak context grounding during Q\&A, and little
operational traceability beyond completion events. Third, a single
general chatbot can improve interactivity, but it typically blends
curriculum, retrieval, assessment, and monitoring concerns into one
prompt surface, making governance and iterative improvement harder.
The Dynavera architecture chooses a middle path: specialized agent roles
within one orchestrated runtime, retrieval-grounded generation, and
persisted session state for reviewability. This trade-off accepts added
system complexity in exchange for improved modularity, clearer
responsibility boundaries, and stronger alignment between training
delivery and management oversight.
\subsection{Learning Origins}\label{learning-origins}
The design and implementation of Dynavera synthesize concepts developed
@ -560,8 +582,18 @@ During guided learning, module content generation, context retrieval,
and assessment output are coordinated in sequence. The curriculum phase
determines structure, the knowledge phase builds role-grounded
instructional content, and the assessment phase constructs evaluative
checkpoints. Progress monitoring then summarizes current status using
persisted session state and completed interactions. This keeps learning
checkpoints. Final assessment grading follows a mixed strategy: multiple
choice responses are deterministically compared against configured
correct options, while non-multiple-choice responses are agent-graded.
Per-question grading outcomes are persisted in session state for review
and feedback rendering.
Progress monitoring then summarizes current status using persisted
session state and completed interactions. In the implemented UI path,
AI monitor inference is only triggered after onboarding completion;
before completion, the system presents a local progress summary.
When available, monitor judgements are informed by prior final-quiz
question/answer evidence and saved grading details. This keeps learning
flow adaptive without abandoning traceability.
Finally, workflow state is persisted throughout execution: user