arXiv subjects

John Roberts

Publications and source records attributed to John Roberts.

5 recordsLinked to original sources

arXiv2025

Building Effective Safety Guardrails in AI Education Tools

There has been rapid development in generative AI tools across the education sector, which in turn is leading to increased adoption by teachers. However, this raises concerns regarding the safety and age-appropriateness of the AI-generated content that is being created for use in classrooms. This paper explores Oak National Academy's approach to addressing these concerns within the development of the UK Government's first publicly available generative AI tool - our AI-powered lesson planning assistant (Aila). Aila is intended to support teachers planning national curriculum-aligned lessons that are appropriate for pupils aged 5-16 years. To mitigate safety risks associated with AI-generated content we have implemented four key safety guardrails - (1) prompt engineering to ensure AI outputs are generated within pedagogically sound and curriculum-aligned parameters, (2) input threat detection to mitigate attacks, (3) an Independent Asynchronous Content Moderation Agent (IACMA) to assess outputs against predefined safety categories, and (4) taking a human-in-the-loop approach, to encourage teachers to review generated content before it is used in the classroom. Through our on-going evaluation of these safety guardrails we have identified several challenges and opportunities to take into account when implementing and testing safety guardrails. This paper highlights ways to build more effective safety guardrails in generative AI education tools including the on-going iteration and refinement of guardrails, as well as enabling cross-sector collaboration through sharing both open-source code, datasets and learnings.

cs.CY↗

arXiv2025

Auto-Evaluation: A Critical Measure in Driving Improvements in Quality and Safety of AI-Generated Lesson Resources

As a publicly funded body in the UK, Oak National Academy is in a unique position to innovate within this field as we have a comprehensive curriculum of approximately 13,000 open education resources (OER) for all National Curriculum subjects, designed and quality-assured by expert, human teachers. This has provided the corpus of content needed for building a high-quality AI-powered lesson planning tool, Aila, that is free to use and, therefore, accessible to all teachers across the country. Furthermore, using our evidence-informed curriculum principles, we have codified and exemplified each component of lesson design. To assess the quality of lessons produced by Aila at scale, we have developed an AI-powered auto-evaluation agent,facilitating informed improvements to enhance output quality. Through comparisons between human and auto-evaluations, we have begun to refine this agent further to increase its accuracy, measured by its alignment with an expert human evaluator. In this paper we present this iterative evaluation process through an illustrative case study focused on one quality benchmark - the level of challenge within multiple-choice quizzes. We also explore the contribution that this may make to similar projects and the wider sector.

cs.CY↗

arXiv2022

Network-level Safety Metrics for Overall Traffic Safety Assessment: A Case Study

Driving safety analysis has recently experienced unprecedented improvements thanks to technological advances in precise positioning sensors, artificial intelligence (AI)-based safety features, autonomous driving systems, connected vehicles, high-throughput computing, and edge computing servers. Particularly, deep learning (DL) methods empowered volume video processing to extract safety-related features from massive videos captured by roadside units (RSU). Safety metrics are commonly used measures to investigate crashes and near-conflict events. However, these metrics provide limited insight into the overall network-level traffic management. On the other hand, some safety assessment efforts are devoted to processing crash reports and identifying spatial and temporal patterns of crashes that correlate with road geometry, traffic volume, and weather conditions. This approach relies merely on crash reports and ignores the rich information of traffic videos that can help identify the role of safety violations in crashes. To bridge these two perspectives, we define a new set of network-level safety metrics (NSM) to assess the overall safety profile of traffic flow by processing imagery taken by RSU cameras. Our analysis suggests that NSMs show significant statistical associations with crash rates. This approach is different than simply generalizing the results of individual crash analyses, since all vehicles contribute to calculating NSMs, not only the ones involved in crash incidents. This perspective considers the traffic flow as a complex dynamic system where actions of some nodes can propagate through the network and influence the crash risk for other nodes. We also provide a comprehensive review of surrogate safety metrics (SSM) in the Appendix A.

cs.CV↗

arXiv2016

Reversing and extended symmetries of shift spaces

The reversing symmetry group is considered in the setting of symbolic dynamics. While this group is generally too big to be analysed in detail, there are interesting cases with some form of rigidity where one can determine all symmetries and reversing symmetries explicitly. They include Sturmian shifts as well as classic examples such as the Thue--Morse system with various generalisations or the Rudin--Shapiro system. We also look at generalisations of the reversing symmetry group to higher-dimensional shift spaces, then called the group of \emph{extended symmetries}. We develop their basic theory for faithful $\mathbb{Z}^{d}$-actions, and determine the extended symmetry group of the chair tiling shift, which can be described as a model set, and of Ledrappier's shift, which is an example of algebraic origin.

math.DS↗

arXiv2010

Modelling Multi Quantum Well Solar Cell Efficiency

The spectral response of quantum well solar cells (QWSCs) is well understood. We describe work on QWSC dark current theory which combined with SR theory yields a system efficiency. A methodology published for single quantum well (SQW) systems is extended to MQW systems in the Al(x) Ga(1-x) As and InGa(0.53x) As(x) P systems. The materials considered are dominated by Shockley-Read-Hall (SRH) recombination. The SRH formalism expresses the dark current in terms of carrier recombination through mid-gap traps. The SRH recombination rate depends on the electron and hole densities of states (DOS) in the barriers and wells, which are well known, and of carrier non-radiative lifetimes. These material quality dependent lifetimes are extracted from analysis of suitable bulk control samples. Consistency over a range of AlGaAs controls and QWSCs is examined, and the model is applied to QWSCs in InGaAsP on InP substrates. We find that the dark currents of MQW systems require a reduction of the quasi Fermi level separation between carrier populations in the wells relative to barrier material, in line with previous studies. Consequences for QWSCs are considered suggesting a high efficiency potential.

cond-mat.mes-hall↗