CollabSense: Can Real Work Become Better Feedback?
Written with Edinburgh Napier University and published open source
CollabSense was written by Ethan Campbell and Ashkan Sami at Edinburgh Napier University, together with Sachin Goyal and Nima Soroush at ClarityLoop.
This research is a collaboration between ClarityLoop and Edinburgh Napier University, and the project materials are available publicly.
Most performance development still depends on delayed memory, forms, self-assessments, and manager interpretation. By the time feedback reaches someone, the work that created it may already be weeks or months old.
ClarityLoop is exploring a different idea: real work already contains signals about how people collaborate, communicate, support others, lead, learn, and grow. If those signals can be recovered responsibly, with evidence and human review, feedback could become more continuous, more specific, and less dependent on memory alone.
CollabSense is an open research study, created with Edinburgh Napier University, testing whether ClarityLoop can identify sentiment, strengths, and growth opportunities from real-world collaboration data. The study does not position AI as an evaluator, a manager substitute, or a decision-maker. It asks a narrower and more useful question: can AI help surface evidence-supported patterns that humans can inspect, challenge, and use for development?
The early answer is encouraging but qualified. Across six experimental configurations using public GitHub conversations from pandas-dev/pandas and kubernetes/kubernetes, the ClarityLoop analysis engine produced stable sentiment estimates with a mean score of 6.63/10, surfaced a large volume of strengths, and produced more plausible growth opportunities once collaboration histories became denser through K-Core filtering and strict peer-to-peer selection.
The strongest finding is not simply that AI can analyse text. It is that context matters. Sparse, one-off interactions were not enough for meaningful coaching signals. Repeated collaboration patterns created better conditions for identifying useful strengths and growth opportunities.[1] [2] [3]
6.63/10
mean sentiment score across more than 3,300 scored interactions
1.1%
baseline Pandas interaction density, too sparse for recurring coaching patterns
35%–75%
density achieved after K-Core filtering and strict peer-to-peer selection in Kubernetes
85%
of sampled growth opportunities judged at least partially actionable in manual review