arXiv subjects

Ilia Rushkin

Publications and source records attributed to Ilia Rushkin.

6 recordsLinked to original sources

arXiv2026

Measuring Creativity in the Age of Generative AI: Distinguishing Human and AI-Generated Creative Performance in Hiring and Talent Systems

Generative AI is rapidly transforming how organizations create value and evaluate talent. While large language models enhance baseline output quality, they simultaneously introduce ambiguity in assessing human creativity, as observable artifacts may be partially or fully AI-generated. This paper reconceptualizes creativity as a distributional and process-based property that emerges under shared constraints and competitive incentives. We introduce a quantitative framework for measuring creativity as novelty in synthesis, operationalized through idea generation and idea transformation within embedding space. Empirical evaluation demonstrates that the proposed metrics align with intuitive judgments of creativity while capturing distinctions that surface-level quality assessments miss. We further identify a structural shift toward bimodal distributions of creative output in AI-mediated environments, with implications for hiring, leadership, and competitive strategy. The findings suggest that in the age of generative AI, distinctiveness rather than fluency becomes the primary signal of human creative capability.

cs.HC↗

arXiv2020

Document Similarity from Vector Space Densities

We propose a computationally light method for estimating similarities between text documents, which we call the density similarity (DS) method. The method is based on a word embedding in a high-dimensional Euclidean space and on kernel regression, and takes into account semantic relations among words. We find that the accuracy of this method is virtually the same as that of a state-of-the-art method, while the gain in speed is very substantial. Additionally, we introduce generalized versions of the top-k accuracy metric and of the Jaccard metric of agreement between similarity models.

cs.CL↗

arXiv2020

Modelling and Using Response Times in Online Courses

Each time a learner in a self-paced online course seeks to answer an assessment question, it takes some time for the student to read the question and arrive at an answer to submit. If multiple attempts are allowed, and the first answer is incorrect, it takes some time to provide a second answer. Here we study the distribution of such "response times." We find that the log-normal statistical model for such times, previously suggested in the literature, holds for online courses. Users who, according to this model, tend to take longer on submits are more likely to complete the course, have a higher level of engagement, and achieve a higher grade. This finding can be the basis for designing interventions in online courses, such as MOOCs, which would encourage "fast" users to slow down.

cs.HC↗

arXiv2018

Time-on-Task Estimation with Log-Normal Mixture Model

We describe a method of estimating a user's time-on-task in an online learning environment. The method is agnostic of the details of the user's mental activity and does not rely on any data except timestamps of user's interactions, accounting for individual user differences. The method is implemented in R (the code is open-source) and has been tested in the data from a large sample of HarvardX MOOCs.

cs.HC↗

arXiv2015

Optimizing the Ptolemaic Model of Planetary and Solar Motion

Ptolemy-s planetary model is an ancient geocentric astronomical model, describing the observed motion of the Sun and the planets. Ptolemy accounted for the deviations of planetary orbits from perfect circles by introducing two small and equal shifts into his model. We show that Ptolemy-s choice of shifts allowed him to approximate the true eccentricity of planetary orbits only in the first order, linear in eccentricity. We show that, if the shifts were tuned in the ratio 5/3, the model precision could be improved substantially, perhaps delaying its rejection. The best achievable precision is quadratic in eccentricities. Although, to achieve it fully, one would have to introduce Ptolemaic shifts in the epicycles as well as in deferents the planets.

physics.hist-ph↗

arXiv2010

Fluid Velocity Fluctuations in a Suspension of Swimming Protists

In dilute suspensions of swimming microorganisms the local fluid velocity is a random superposition of the flow fields set up by the individual organisms, which in turn have multipole contributions decaying as inverse powers of distance from the organism. Here we show that the conditions under which the central limit theorem guarantees a Gaussian probability distribution function of velocities are satisfied when the leading force singularity is a Stokeslet, but are not when it is any higher multipole. These results are confirmed by numerical studies and by experiments on suspensions of the alga Volvox carteri, which show that deviations from Gaussianity arise from near-field effects.

physics.bio-ph↗