A Gravity Dual and LHC Study of Single-Sector Supersymmetry Breaking

We propose a gravitational dual of ``single-sector'' models of supersymmetry breaking which contain no messenger sector and naturally explain the scale of supersymmetry breaking and the fermion mass hierarchy. In five dimensions these models can be given a simple interpretation. Inspired by flux-background solutions of type IIB supergravity, a metric background that deviates from AdS_5 in the IR breaks supersymmetry, while the fermion mass hierarchy results from the wavefunction overlap of bulk fermions with a UV-confined Higgs field. The first and second generation squarks and sleptons, which are localized near the IR brane, directly feel the supersymmetry breaking and obtain masses of order 10 TeV. These are interpreted as composite states of the dual 4D theory. The gauginos and third generation squarks and sleptons are elementary states that obtain soft masses of order 1 TeV at the loop level via direct gauge mediation. This particle spectrum leads to distinctive signatures at the LHC, similar to the usual gauge mediation with a neutralino NLSP that decays promptly to a gravitino LSP, but with lower event rates. Nevertheless we show that with 1-10 fb^{-1} of LHC data "single-sector" models can easily be detected above background and distinguished from conventional gravity and gauge mediation.Comment: 35 pages, 6 figures, LaTe

Implementing Learning Principles with a Personal AI Tutor: A Case Study

Effective learning strategies based on principles like personalization, retrieval practice, and spaced repetition are often challenging to implement due to practical constraints. Here we explore the integration of AI tutors to complement learning programs in accordance with learning sciences. A semester-long study was conducted at UniDistance Suisse, where an AI tutor app was provided to psychology students taking a neuroscience course (N=51). After automatically generating microlearning questions from existing course materials using GPT-3, the AI tutor developed a dynamic neural-network model of each student's grasp of key concepts. This enabled the implementation of distributed retrieval practice, personalized to each student's individual level and abilities. The results indicate that students who actively engaged with the AI tutor achieved significantly higher grades. Moreover, active engagement led to an average improvement of up to 15 percentile points compared to a parallel course without AI tutor. Additionally, the grasp strongly correlated with the exam grade, thus validating the relevance of neural-network predictions. This research demonstrates the ability of personal AI tutors to model human learning processes and effectively enhance academic performance. By integrating AI tutors into their programs, educators can offer students personalized learning experiences grounded in the principles of learning sciences, thereby addressing the challenges associated with implementing effective learning strategies. These findings contribute to the growing body of knowledge on the transformative potential of AI in education.Comment: 17 pages, 7 figure