LLM-Augmented IT Project Management : Intelligent Risk Assessment and Automated Documentation Systems
Bibliographic record
Abstract
IT project management continues to experience high failure rates driven by inadequate risk visibility and documentation inefficiencies. While traditional frameworks provide structured processes, they struggle to manage the volume and velocity of modern project data. This study proposes the LLM-Augmented Risk and Documentation Integration (LARDI) Framework, a structured model embedding Large Language Models into formal IT risk and governance workflows. Through systematic literature synthesis and thematic analysis, the research identifies operational capabilities, governance safeguards, and measurable performance indicators for responsible deployment. A hybrid mathematical risk scoring model is introduced, integrating probabilistic exposure with contextual intelligence derived from unstructured artifacts. The findings position LLMs not as autonomous decision-makers but as augmented intelligence systems that enhance early risk detection, documentation accuracy, and compliance readiness. Ethical, security, and organizational change considerations are examined to ensure sustainable implementation. This research contributes a formalized integration architecture and evaluation metrics, establishing a foundation for empirical validation and advancing the discourse on AI-enabled project governance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".