Introductory programming courses remain challenging for many students, which motivates educators to adopt gamification to enhance engagement and learning. More recent work explores adaptive gamification, where game elements and task flow are tailored to individual learners. A key requirement for such adaptation is the ability to predict student success on upcoming tasks. Using a dataset of task attempts collected from a gamified introductory programming activity, we examine the predictive value of coarse-grained knowledge components, task difficulty, and dynamic student performance features. The results show that behavioral signals are substantially more informative than task properties: a student's prior success history and their position within a lesson sequence are the strongest predictors of future correctness. Although advanced topics such as file handling and structures are associated with increased failure rates, their impact is secondary to students' evolving engagement patterns. These findings highlight the role of momentum and practice effects in gamified programming environments and suggest that adaptive systems should prioritize real-time learner progression when providing instructional support. Dataset and the code for our experiments is available at https://osf.io/cajby.

Conversational commerce agents that personalize assistance based on a user’s transactional state (cart contents, checkout progress, order completion) must model that state correctly, or downstream adaptive behavior will be misaligned with the user’s actual journey. We call mismatches between an agent’s claims and the observable event history journey hallucinations, and study a lightweight verification framework that reconstructs a minimal transactional user model from execution logs and checks agent claims against deterministic invariants. On 90 real sessions across four foundation models, trace-aware prompting reaches 99.5–100% user-state accuracy at 84–99% coverage, while unconstrained prompting produces unsupported state assertions at rates up to 8.5%. In a between-subjects user study (N = 42), verified responses were judged more trustworthy (p =.008, r =.43), better at reflecting journey understanding (p =.039, r =.32), and more often factually correct (p <.001, r =.56). The framework provides a practical reliability layer for transactional user-state modeling, helping personalization and dialog policies operate on verified, not hallucinated, user states.

ABSTRACT Explainable AI (XAI) is essential for building trust in Deep Neural Networks (DNNs). SHAP (SHapley Additive exPlanations) is a well‐known XAI technique for attributing feature importance, but it struggles with exponential computational complexity as the number of features increases. Various approximation methods have been suggested, but they compromise SHAP's theoretical principles. We introduce AA‐SHAP, a novel approach that derives superpixel affinity from the explained model's internals to identify and group superpixels. AA‐SHAP constructs a relevance‐consistency affinity between superpixel interdependence, enabling much faster SHAP calculations on a reduced set of meta‐superpixels while outperforming previous methods in explanation faithfulness. Exact Shapley values are computed on the reduced meta‐superpixel game, preserving all axiomatic guarantees within the aggregated feature space. Evaluated across multiple datasets, both convolutional and transformer classification architectures show that AA‐SHAP produces more faithful attributions than competing methods while improving computational speed and maintaining SHAP's theoretical axioms. The source code is available at https://github.com/vhasic/AA‐SHAP .

ABSTRACT Explainable AI (XAI) is crucial for fostering human trust in deep neural network (DNN) predictions, particularly in tasks like image classification. Multiple surveys exist on XAI methodologies, however, the practical usability and reproducibility of these methods remain largely unexplored. This paper addresses this gap by conducting a systematic survey of recent XAI papers published in leading computer vision and AI conferences and journals. We categorize these works, identify prevalent datasets and evaluation metrics, and analyse the associated code repositories. Our analysis reveals that almost 95% of the surveyed codebases are research prototypes rather than published releases, and a concerning majority of two‐thirds of them exhibit inconsistencies with their corresponding publications. These findings highlight the challenges in benchmarking new XAI methods against existing ones and explain the slow adoption of state‐of‐the‐art research in real‐world applications. This paper aims to underscore the importance of releasing well‐documented, readily usable code alongside XAI research to foster a more robust and reproducible ecosystem, ultimately facilitating the development and deployment of trustworthy AI systems. The results of this study are presented on an interactive website Interactive‐XAI.

Abstract Epilepsy is a life threatening neurological disorder. The person with epilepsy suffers from recurrent seizures. Sudden emission of electrical signal in the nerves of the human brain is called seizure event. The most widely used method for diagnosing epilepsy is analysing electroencephalogram signals in short called as EEG signals collected from the scalp of the patient. The EEG data are normally used for seizure detection. If the recurrent seizure signals are detected in the input EEG dataset, then it can be considered as the presence of epilepsy disorder. Manual inspection of seizure signals in the EEG data is a laborious process. An automated system is very crucial for the neurologists to identify seizures. In this paper, an automated seizure detection method is presented using deep learning method, pre‐trained convolutional neural network architecture. Freely available EEG dataset from Temple University Hospital database is used for the study. The pre‐trained CNN networks, VGGNet and ResNet are used for classifying the seizure activities from non‐seizure activities. CNNs are extremely good in learning the features of the input data. A very large dataset from TUH is provided as input to the multiple layers of CNN model. The same data is fed to VGGNet and ResNet models. The results of CNN, VGGNet and ResNet models are assessed using performance metrics accuracy, AUC, precision and recall. All the three models gave extremely good performance compared to state‐of‐the‐art works in the literature. In comparison VGGNet performed with little higher results giving 97% accuracy, 96% AUC, 97% precision and 79% recall.

Scanning Electron Microscope (SEM) images play a crucial role in defect detection and analysis in the semiconductor manufacturing process. However, traditional deep learning methods for image classification act as black boxes, hindering expert trust and impeding model debugging and improvement. This paper tackles this problem by applying state-of-the-art explainability methods tailored to expert needs. In addition, we present Carinthia-S, the first expert-validated dataset providing ground truth segmentation masks for SEM images of semiconductor manufacturing defects, used as ground truth to evaluate the explainability methods. Our results show that our proposed automatic ground truth segmentation approach achieved impressive performance with 91.33% correctly segmented images, and that experts are satisfied with the provided explanations. The Carinthia-S dataset is made publicly available.

The increasing complexity of semiconductor manufacturing calls for reliable and interpretable machine learning systems that can support decision-making in real time. In this work, we propose a virtual metrology system for predicting multiple output parameters in two physical vapor deposition processes—AlCu and WTi—based on real-world data collected from Infineon Technologies. We explore the effectiveness of machine learning models for multi-output regression and evaluate three model-based feature selection approaches alongside the projective selection method (ProjSe), a recent technique designed specifically for multi-output scenarios. Our analysis focuses on model accuracy, stability under data variation, and computational efficiency. The results show that the Extreme Gradient Boosting method achieves the highest prediction accuracy, while ProjSe provides a stable and significantly faster solution for feature selection, making it a promising candidate for industrial applications where speed and robustness are essential.

This paper introduces affordance-based explanations of robot navigational decisions. The rationale behind affordance-based explanations draws on the theory of affordances, a principle rooted in ecological psychology that describes potential actions the objects in the environment offer to the robot. We demonstrate how affordances can be incorporated into visual and textual explanations for common robot navigation and path-planning scenarios. Furthermore, we formalize and categorize the concept of affordance-based explanations and connect it to existing explanation types in robotics. We present the results of a user study that shows participants to be, on average, highly satisfied with visual-textual, i.e., multimodal, affordance-based explanations of robot navigation. Furthermore, we investigate the complexity of different types of textual affordance-based explanations. Our research contributes to the expanding domain of explainable robotics, focusing on explaining robot actions in navigation.

Introductory programming courses are widely known for their difficulty among students.Success in courses is commonly measured in the form of final grades, which might not capture the challenges students face during their learning process.In this paper, we predict students' success and their future compiler errors based on previously made errors.Furthermore, we examine the effect of applying two clustering techniques before making the predictions and identify key weeks and errors that have the greatest impact on predictions.Experimental results show that students' compiler errors observed through the semester are an important predictor of students' achievement and future struggles.Predictions are further improved using sentence encoder-generated embeddings with K-Means algorithm.Our study suggests that students' errors, particularly the most recent ones, enable meaningful clustering that enhances performance prediction after only three weeks of the semester.

The choices made by autonomous robots in social settings bear consequences for humans and their presumptions of robot behavior. Explanations can serve to alleviate detrimental impacts on humans and amplify their comprehension of robot decisions. We model the process of explanation generation for robot navigation as an automated planning problem considering different possible explanation attributes. Our visual and textual explanations of a robot’s navigation are influenced by the robot’s personality. Moreover, they account for different contextual, environmental, and spatial characteristics. We present the results of a user study demonstrating that users are more satisfied with multimodal than unimodal explanations. Additionally, our findings reveal low user satisfaction with explanations of a robot with extreme personality traits. In conclusion, we deliberate on potential future research directions and the associated constraints. Our work advocates for fostering socially adept and safe autonomous robot navigation.

Abstract The identification of bacterial colonies is deemed to be crucial in microbiology as it helps in identifying specific categories of bacteria. The careful examination of colony morphology plays a crucial role in microbiology laboratories for the identification of microorganisms. Quantifying bacterial colonies on culture plates is a necessary task in Clinical Microbiology Laboratories, but it can be time‐consuming and susceptible to inaccuracies. Therefore, there is a need to develop an automated system that is both dependable and cost‐effective. Advancements in Deep Learning have played a crucial role in improving processes by providing maximum accuracy with a negligible amount of error. This research proposes an automated technique to extract the bacterial colonies using SegNet, a semantic segmentation network. The segmented colonies are then counted with the assistance of blob counter to accomplish the activity of colony counting. Furthermore, to ameliorate the proficiency of the segmentation network, the network weights are optimized using a swarm optimizer. The proposed methodology is both cost‐effective and time‐efficient, while also providing better accuracy and precise colony counts, ensuring the elimination of human errors involved in traditional colony counting techniques. The investigative assessments were carried out on three distinct sets of data: Microorganism, DIBaS, and tailored datasets. The results obtained from these assessments revealed that the suggested framework attained an accuracy rate of 88.32%, surpassing other conventional methodologies with the utilization of an optimizer.

For users to trust planning algorithms, they must be able to understand the planner's outputs and the reasons for each action selection. This output does not tend to be user-friendly, often consisting of sequences of parametrised actions or task networks. And these may not be practical for non-expert users who may find it easier to read natural language descriptions. In this paper, we propose PlanVerb, a domain and planner-independent method for the verbalization of task plans. It is based on semantic tagging of actions and predicates. Our method can generate natural language descriptions of plans including causal explanations. The verbalized plans can be summarized by compressing the actions that act on the same parameters. We further extend the concept of verbalization space, previously applied to robot navigation, and apply it to planning to generate different kinds of plan descriptions for different user requirements. Our method can deal with PDDL and RDDL domains, provided that they are tagged accordingly. Our user survey evaluation shows that users can read our automatically generated plan descriptions and that the explanations help them answer questions about the plan.

Recent research in AI ethics has put forth explainability as an essential principle for AI algorithms. However, it is still unclear how this is to be implemented in practice for specific classes of algorithms—such as motion planners. In this paper we unpack the concept of explanation in the context of motion planning, introducing a new taxonomy of kinds and purposes of explanations in this context. We focus not only on explanations of failure (previously addressed in motion planning literature) but also on contrastive explanations—which explain why a trajectory A was returned by a planner, instead of a different trajectory B expected by the user. We develop two explainable motion planners, one based on optimization, the other on sampling, which are capable of answering failure and constrastive questions. We use simulation experiments and a user study to motivate a technical and social research agenda.

Motion planning is a hard problem that can often overwhelm both users and designers: due to the difficulty in understanding the optimality of a solution, or reasons for a planner to fail to find any solution. Inspired by recent work in machine learning and task planning, in this paper we are guided by a vision of developing motion planners that can provide reasons for their output—thus potentially contributing to better user interfaces, debugging tools, and algorithm trustworthiness. Towards this end, we propose a preliminary taxonomy and a set of important considerations for the design of explainable motion planners, based on the analysis of a comprehensive user study of motion planning experts. We identify the kinds of things that need to be explained by motion planners ("explanation objects"), types of explanation, and several procedures required to arrive at explanations. We also elaborate on a set of qualifications and design considerations that should be taken into account when designing explainable methods. These insights contribute to bringing the vision of explainable motion planners closer to reality, and can serve as a resource for researchers and developers interested in designing such technology.

In automated planning, the need for explanations arises when there is a mismatch between a proposed plan and the user’s expectation. We frame Explainable AI Planning as an iterative plan exploration process, in which the user asks a succession of contrastive questions that lead to the generation and solution of hypothetical planning problems that are restrictions of the original problem. The object of the exploration is for the user to understand the constraints that govern the original plan and, ultimately, to arrive at a satisfactory plan. We present the results of a user study that demonstrates that when users ask questions about plans, those questions are usually contrastive, i.e. “why A rather than B?”. We use the data from this study to construct a taxonomy of user questions that often arise during plan exploration. Our approach to iterative plan exploration is a process of successive model restriction. Each contrastive user question imposes a set of constraints on the planning problem, leading to the construction of a new hypothetical planning problem as a restriction of the original. Solving this restricted problem results in a plan that can be compared with the original plan, admitting a contrastive explanation. We formally define model-based compilations in PDDL2.1 for each type of constraint derived from a contrastive user question in the taxonomy, and empirically evaluate the compilations in terms of computational complexity. The compilations were implemented as part of an explanation framework supporting iterative model restriction. We demonstrate its benefits in a second user study.

We present a novel approach for decreasing state uncertainty in planning prior to solving the planning problem. This is done by making predictions about the state based on currently known information, using machine learning techniques. For domains where uncertainty is high, we define an active learning process for identifying which information, once sensed, will best improve the accuracy of predictions. We demonstrate that an agent is able to solve problems with uncertainties in the state with less planning effort compared to standard planning techniques. Moreover, agents can solve problems for which they could not find valid plans without using predictions. Experimental results also demonstrate that using our active learning process for identifying information to be sensed leads to gathering information that improves the prediction process.

Pushing is a common task in robotic scenarios. In real-world environments, robots need to manipulate various unknown objects without previous experience. We propose a data-driven approach for learning local inverse models of robot-object interaction for push manipulation. The robot makes observations of the object behaviour on the fly and adapts its movement direction. The proposed model is probabilistic, and we update it using maximum a posteriori (MAP) estimation. We test our method by pushing objects with a holonomic mobile robot base. Validation of results over a diverse object set demonstrates a high degree of robustness and a high success rate in pushing objects towards a fixed target and along a path compared to previous methods. Moreover, based on learned inverse models, the robot can learn object properties and distinguish between different object behaviours when they are pushed from different sides.

In real world environments the state is almost never completely known. Exploration is often expensive. The application of planning in these environments is consequently more difficult and less robust. In this paper we present an approach for predicting new information about a partially-known state. The state is translated into a partially-known multigraph, which can then be extended using machine-learning techniques. We demonstrate the effectiveness of our approach, showing that it enhances the scalability of our planners, and leads to less time spent on sensing actions.