Abstract
We present a formally verified proof of the correctness and IND-CCA security of ML-KEM, the Kyber-based Key Encapsulation Mechanism (KEM) undergoing standardization by NIST. The proof is machine-checked in EasyCrypt and it includes: 1) A formalization of the correctness (decryption failure probability) and IND-CPA security of the Kyber base public-key encryption scheme, following Bos et al. at Euro S&P 2018; 2) A formalization of the relevant variant of the Fujisaki-Okamoto transform in the Random Oracle Model (ROM), which follows closely (but not exactly) Hofheinz, Hovelmanns and Kiltz at TCC 2017; 3) A proof that the IND-CCA security of the ML-KEM specification and its correctness as a KEM follows from the previous results; 4) Two formally verified implementations of ML-KEM written in Jasmin that are provably constant-time, functionally equivalent to the ML-KEM specification and, for this reason, inherit the provable security guarantees established in the previous points. The top-level theorems give self-contained concrete bounds for the correctness and security of ML-KEM down to (a variant of) Module-LWE. We discuss how they are built modularly by leveraging various EasyCrypt features.

Abstract
Background: Many progranunmg environments include automated feedback in the form of hints to help novices learn autonomously. Some experimental studies investigated the impact of automated liints in the immediate, performance and learning retention in that context. Automated feedback is also becoming a popular research topic in the context of formal specification languages, but so far no experimental studies have been conducted to assess its impact while learning such languages. Objective: We aim to investigate the impact of different types of automated hints while learning a formal specification language, not only in terms of immediate performance and learning retention, but also in the emotional response of the students. Method: We conducted a simple one-factor randomised experiment in 2 sessions involving 85 BSc students majoring in CSE. In the 1st session students were divided in 1 control group and 3 experimental groups, each receiving a different type of hint while learning to specify simple, requirements with the Alloy formal specification language. To assess the impact of hints on learning retention, in the 2nd session, 1 week later, students had no hints while formalising requirements. Before and after each session the students answered a standard self-reporting emotional survey to assess their emotional response to the experiment. Results: Of the 3 types of hints considered, only those pointing to the precise location of an error had a positive impact on the immediate performance and none had significant impact in learning retention. Hint availability also causes a significant impact on the emotional response, but no significant emotional :impact exists once hints are no longer available (i.e. no deprivation effects were detected). Conclusion: Although none of the evaluated hints had an impact on learning retention, learning a formal specification language with an environment that provides hints with precise error locations seems to contribute to a better overall experience without apparent drawbacks. Further studies are needed to investigate if other kind of feedback, namely hints combined with some sort of self explanation prompts, can have a positive impact in learning retention.

Abstract
This panel commemorates the 50th anniversary of the IFIP TC2 Working Conference on Command Languages (CCL) and the 30th anniversary of the workshop series on Design Specification and Verification of Interactive Systems (DSV-IS), and uses that opportunity to position EICS within the HCI community. The discussion traces the origins of the EICS conference, from the union of seminal conferences to its current status and looks forward into its (possible) future. Reflecting on its contributions to the evolution of HCI methodologies, tools, and practices, the panel highlights the conference's role and impact on shaping the engineering of interactive systems.

Abstract
This paper leverages the outcomes of the first workshop on HCI Engineering Education [4] to create an online repository where the community can share content relevant to HCI. The repository takes advantage of the functionalities of the Git file versioning system to support presenting and adding content. The paper describes the structure of the repository and the process for adding new content. In addition, we propose an adaptation of the framework for presenting teaching samples, supporting more flexibility in the application of educational material for different teaching objectives. The new presentation format starts with describing a design problem and emphasises the students' applied understanding of conceptual and theoretical knowledge. The presentation format is demonstrated and discussed by the example of an end-user design tool for mobile data collection.

Abstract
The second workshop on HCI Engineering Education continued the effort of the IFIP Working Group 2.7/13.4 on User Interface Engineering by discussing the issues and identifying the opportunities in teaching and learning Human-Computer Interaction (HCI) Engineering. The workshop attracted eight papers covering different teaching contexts, ranging from massive university courses, passing through different teaching experiences in specific academic curricula, and even teaching engineering concepts to children. In addition, the workshop received input for improving and adapting the repository material to the dynamic nature of this field. The discussion after the presentation of the contributions focused on how to model competencies, the support to interdisciplinary work, the overall course design, the recruitment of the students and the provision of educational resources, paving the way for further editions of the workshop.

Abstract
The use of model checking tools allows for the formal verification of properties over models of systems, improving their robustness. However, these tools are challenging to use, and their results require much work of interpretation to communicate to stakeholders. To address this issue, the IVY Workbench offers a plethora of options to make the process of creating and understanding the models, properties and results of the verification process more accessible, with a particular focus on interactive computing systems. Despite this, there is still a significant requirement of expertise to use the tool. To solve this, an approach to provide structured natural language explanations for the results of model checking-based tools is being developed, to be later incorporated into the IVY Workbench. This paper presents the current state of the approach's development, stating its objective and what results can already be achieved.