Learning Progress Research Articles

SAFLFusionGait: Gait recognition network with separate attention and different granularity feature learnability fusion

Gait recognition, an essential branch of biometric identification, uses walking patterns to identify individuals. Despite its effectiveness, gait recognition faces challenges such as vulnerability to changes in appearance due to factors like angles and clothing conditions. Recent progress in deep learning has greatly enhanced gait recognition, especially through methods like deep convolutional neural networks, which demonstrate impressive performance. However, current approaches often overlook the connection between coarse-grained and fine-grained features, thereby restricting their overall effectiveness. To address this limitation, we propose a new framework for gait recognition framework that combines deep-supervised fine-grained separation with coarse-grained feature learnability. Our framework includes the LFF module, which consists of the SSeg module for fine-grained information extraction and a mechanism for fusing coarse-grained features. Furthermore, we introduce the F-LCM module to extract local disparity features more effectively with learnable weights. Evaluation on CASIA-B and OU-MVLP datasets shows superior performance compared to classical networks.

Transforming Computer Practice Teaching through Virtual Reality: Trends and Future Directions

The integration of Virtual Reality (VR) in computer education has acquired significant momentum due to its ability to provide immersive and interactive learning experiences. This paper analyzes current trends, challenges, and potential advancements in the utilization of virtual reality for practical computer science education. We aim to investigate how virtual reality (VR) enhances experiential learning through key characteristics such as immersion, engagement, and presence, while also examining the influence of emerging technologies like artificial intelligence (AI) and VR-augmented reality (AR) hybrid systems on the progression of personalized learning. We assess the benefits of VR in developing practical skills and critical thinking through a comprehensive analysis of recent advancements in VR hardware, software, and their applications inside a safe, virtual environment. Additionally, we assess the integration of virtual reality (VR) with learning management systems (LMS) and its trans disciplinary applications, as well as ethical concerns regarding accessibility and data protection.

Quantitative analysis of student learning progress in online curses during the COVID-19 Pandemic

Abstract The pandemic of COVID-19 forced the transition of education to online platforms. This shift allowed for a more in-depth study of the learning process of students, as it provided access to information about their activities and interactions with the course’s contents [1-3]. In this work, we apply quantitative methods to analyze the student learning progress of students for two first year courses of the B.Sc. degree in Physics (Universidad Católica del Norte). The data are collected from activity logs of digital platforms during Academic year 2021, which were employed to characterize the student learning habits along these processes (experience, selectivity and workflow). Statistical analysis is based on the distribution of the time spent on learning activities, such as the daily time distribution and the cumulative attention per online materials. The results show that the learning process of students in online courses is complex and adaptive. We report statistical patterns similar to those found in financial markets. These empirical results provide evidence about the importance of economic principles and methods in the understanding of the teaching-learning process.

Using eye tracking to assess learning of a multifunction prosthetic hand: an exploratory study from a rehabilitation perspective

BackgroundEye tracking technology not only reveals the acquisition of visual information at fixation but also has the potential to unveil underlying cognitive processes involved in learning to use a multifunction prosthetic hand. It also reveals gaze behaviours observed during standardized tasks and self-chosen tasks. The aim of the study was to explore the use of eye tracking to track learning progress of multifunction hands at two different time points in prosthetic rehabilitation.MethodsThree amputees received control training of a multifunction hand with new control strategy. Detailed description of control training was collected first. They wore Tobii Pro2 eye-tracking glasses and performed a set of standardized tasks (required to switch to different grips for each task) after one day of training and at one-year-follow-up (missing data for Subject 3 at the follow up due to socket problem). They also performed a self-chosen task (free to use any grip for any object) and were instructed to perform the task in a way how they would normally do at home. The gaze-overlaid videos were analysed using the Tobii Pro Lab and the following metrics were extracted: fixation duration, saccade amplitude, eye-hand latency, fixation count and time to first fixation.ResultsDuring control training, the subjects learned 3 to 4 grips. Some grips were easier, and others were more difficult because they forgot or were confused with the switching strategies. At the one-year-follow-up, a decrease in performance time, fixation duration, eye-hand latency, and fixation count was observed in Subject 1 and 2, indicating an improvement in the ability to control the multifunction hand and a reduction of cognitive load. An increase in saccade amplitude was observed in both subjects, suggesting a decrease in difficulty to control the prosthetic hand. During the standardized tasks, the first fixation of all three subjects were on the multifunction hand in all objects. During the self-chosen tasks, the first fixations were mostly on the objects first.ConclusionThe qualitative data from control training and the quantitative eye tracking data from clinical standardized tasks provided a rich exploration of cognitive processing in learning to control a multifunction hand. Many prosthesis users prefer multifunction hands and with this study we have demonstrated that a targeted prosthetic training protocol with reliable assessment methods will help to lay the foundation for measuring functional benefits of multifunction hands.

GLAN: Global Local Attention Network for Thoracic Disease Classification to Enhance Heart Failure Diagnosis

ABSTRACTChest x‐ray (CXR) is an effective method for diagnosing heart failure, and identifying important features such as cardiomegaly, effusion, and edema on patient chest x‐rays is significant for aiding the treatment of heart failure. However, manually identifying a vast amount of CXR data places a huge burden on physicians. Deep learning's progression has led to the utilization of this technology in numerous research aimed at tackling these particular challenges. However, many of these studies utilize global learning methods, where the contribution of each pixel to the classification is considered equal, or they overly focus on small areas of the lesion while neglecting the global context. In response to these issues, we propose the Global Local Attention Network (GLAN), which incorporates an improved attention module on a branched structure. This enables the network to capture small lesion areas while also considering both local and global features. We evaluated the effectiveness of the proposed model by testing it on multiple public datasets and real‐world datasets. Compared to the state‐of‐the‐art methods, our network structure demonstrated greater accuracy and effectiveness in the identification of three key features: cardiomegaly, effusion, and edema. This provides more targeted support for diagnosing and treating heart failure.

From literacy to learning: The sequential mediation of attitudes and enjoyment in AI-assisted EFL education

Artificial intelligence (AI) has become increasingly prominent in English as a Foreign Language (EFL) education. However, research on the factors influencing learners' sustained engagement with AI-assisted learning is scarce. This study bridges this gap by investigating the impact of AI literacy, learner attitudes toward AI-assisted learning, and foreign language enjoyment (FLE) on Chinese EFL learners' continuance intention (CI) for AI-assisted EFL learning. A survey of 417 EFL learners was conducted, and structural equation modeling was utilized to elucidate the relationships between these variables. The findings reveal that AI literacy, learner attitudes, and FLE significantly predict the CI. Moreover, AI literacy indirectly influences CI by mediating learner attitudes and FLE. Finally, FLE mediates the relationship between learner attitudes and CI. The study verifies a sequential mediation model, providing new insights into EFL learners' cognitive and affective traits and extending current models on the continuance intention in AI-assisted EFL learning. The research underscores the necessity for educators and technology developers to consider learners' interests and feedback, which are crucial for the sustainable progress of AI-assisted EFL learning.

Exploring learning characteristics and progression ofGPtrainees based in regional, rural and remote settings:Aqualitative study.

Globally, most doctors train and work in metropolitan areas but a key strategy for developing the rural health workforce is expanding rural training. The aim of this study was to describe the scope and quality of learning along with skill acquisition of GP trainees based in regional, rural, and remote settings. Regional, rural and remote settings in Queensland Australia excluding Brisbane. Thirty-seven general practice trainees who had undertaken their first community placement were recruited from regional, rural and remote learning settings within Queensland. Qualitative descriptive methodology based on constructionist epistemology was employed which allowed for the exploration of current GP trainees' experiences. Trainees were interviewed and data were thematically analysed as to the scope and quality of learning by the setting of training. Learning experiences were then mapped against the Dreyfus and Dreyfus model to explore skill acquisition. In terms of the scope and quality of learning, rural and remote trainees mainly focused on the diverse and unique (sometimes challenging) experiences their setting offered compared with regionally based trainees. Mapping of the trainee comments to the Dreyfus model of skill acquisition demonstrated that regardless of setting, equivalent learning occurred by GP trainees. This exploratory study provides evidence that rural and remote learning may provide a more diverse and challenging experience. It suggests an equivalence of quality of education and skill acquisition across settings rural, regional and remote. This suggests that the training distribution policies may not disadvantage GP trainees and the scope and quality of more remote learning may increase uptake of remote placements.

Technology Enhanced Assessment in Higher Education

Research indicates that assessment of learning and assessment of learning outcomes are the biggest challenges that teachers in higher education (HE) face. Traditional assessment practices do not work when learning and teaching is supported with technologies, which is inevitable in HE today. Teachers lack digital competence in designing assessment strategies and applying digital technologies for assessment of learning process and learning outcomes. DIGI-PROF project aims to address these challenges by introducing innovative learning and teaching practices that promote transparent technology enhanced assessment for online learning. This paper seeks to identify how teachers have progressed with application of digital technologies to support technology enhanced assessment of learning process and learning outcomes. The research revealed which technologies teachers apply for technology enhanced assessment of learning process (voice/video/text communication channels, file-sharing services, emails, and online quizzes), and how teachers use these technologies to facilitate learning process (providing feedback, applying technologies for peer assessment, and monitoring of students’ learning). The study opened perspectives for further investigation on the use of learning analytics data to monitor and track students' learning progress.

In the Belly of Text Complexity: Unravelling the Nexus between Lexical Density and Readability

This study set out to explore the link between lexical density (LD) as a tool for determining text complexity and the general readability of texts (in this study, texts assigned to Senior High School learners in Ghana). To clearly establish this link, the levels of LD of assigned texts have to be first determined, and then, compared with their corresponding readability values. To that end, Ure’s (1971) LD formula and Halliday’s (1985b) LD formula have been used to determine the LD values of the sampled texts. Gunning’s (1952) and Flesch’s (1948) readability indices have been used to determine the readability values of same assigned texts. LD values for the sampled texts were found to be generally very high (above the widely agreed minimum of 40%); as their corresponding readability values were low. The correlation between LD and readability is therefore adequately revealed: high LD translates directly into low readability while low LD translates directly into high readability. Implicatively, low readability of texts definitely affects learner progress negatively. Keywords: lexical density, readability, Senior High School learners in Ghana, Ure’s (1971) LD formula, Halliday’s (1985) LD formula, Gunning’s (1952) readability index, Flesch’s (1948) readability index

Teachers’ Perceptions towards English Language Teaching Pedagogy in Secondary Schools of Nepal

Recently research on English language teaching pedagogy has been receiving increased attention throughout the world, with a primary focus on classroom teaching strategies. This qualitative research study aims to explore the narratives of English language teachers about the use of English language teaching pedagogy in secondary schools in Nepal. The participants purposively selected for this study were four English language teachers teaching at the secondary level in public schools of Kanchanpur district. The researcher used narrative inquiry as a research method in which interview protocols with open-ended questions were employed as research tools to obtain information. The findings of the study showed that secondary English teachers have been shifting their classroom pedagogy from traditional teacher-centred approaches to modern innovative student-centred approaches. The study further revealed that teachers keep the knowledge of both teacher-centred and student-centred methods and techniques but practise them in the classrooms in their ways due to the limitations of prescribed methods. The study also revealed students’ level of English as the main barrier to their learning progress. The implication of the study is that the teachers should create more learning opportunities and environments inside and outside the classroom to increase students’ maximum time engagement in English learning-based activities that provide them with more exposure to English and enhances their learning.

Harnessing Deep Learning Methods for Voltage-Gated Ion Channel Drug Discovery.

Voltage-gated ion channels (VGICs) are pivotal in regulating electrical activity in excitable cells and are critical pharmaceutical targets for treating many diseases including cardiac arrhythmia and neuropathic pain. Despite their significance, challenges such as achieving target selectivity persist in VGIC drug development. Recent progress in deep learning, particularly diffusion models, has enabled the computational design of protein binders for any clinically relevant protein based solely on its structure. These developments coincide with a surge in experimental structural data for VGICs, providing a rich foundation for computational design efforts. This review explores the recent advancements in computational protein design using deep learning and diffusion methods, focusing on their application in designing protein binders to modulate VGIC activity. We discuss the potential use of these methods to computationally design protein binders targeting different regions of VGICs, including the pore domain, voltage-sensing domains, and interface with auxiliary subunits. We provide a comprehensive overview of the different design scenarios, discuss key structural considerations, and address the practical challenges in developing VGIC-targeting protein binders. By exploring these innovative computational methods, we aim to provide a framework for developing novel strategies that could significantly advance VGIC pharmacology and lead to the discovery of effective and safe therapeutics.

PERANCANGAN MEDIA PEMBELAJARAN PENGENALAN BENDERA PADA ANAK KELAS 1 SDIT EL FATAH BERBASIS CONSTRUCT 2

This study aims to design and develop a learning application focused on introducing flags to first-grade students at SDIT EL Fatah. The application is designed using the Construct 2 platform, a tool that allows the development of interactive applications without requiring deep programming skills. This application integrates various interactive features that include learning materials, quizzes, user profiles, and usage instructions. The learning materials are presented in engaging and easy-to-understand slides, while the quizzes are designed to test and reinforce students' understanding of the material they have learned. The user profile feature allows students to track their learning progress, and the usage instructions provide clear guidance for navigating the application.The research method used is Research and Development (R&D), which includes steps such as identifying potential and problems, data collection, product design, design validation, design revision, product testing, product revision, and mass production. The results of this study indicate that this flag introduction learning application is effective in increasing students' interest and understanding of historical material. The application also received positive responses from teachers and students, indicating that the use of technology in learning can enhance student interaction and engagement.Therefore, this application is expected to become an innovative and effective learning solution, particularly in the context of primary education in Indonesia. This study also contributes to the development of technology-based learning methods that can be applied to various other subjects in primary schools

Social Media Topic Classification on Greek Reddit

Text classification (TC) is a subtask of natural language processing (NLP) that categorizes text pieces into predefined classes based on their textual content and thematic aspects. This process typically includes the training of a Machine Learning (ML) model on a labeled dataset, where each text example is associated with a specific class. Recent progress in Deep Learning (DL) enabled the development of deep neural transformer models, surpassing traditional ML ones. In any case, works of the topic classification literature prioritize high-resource languages, particularly English, while research efforts for low-resource ones, such as Greek, are limited. Taking the above into consideration, this paper presents: (i) the first Greek social media topic classification dataset; (ii) a comparative assessment of a series of traditional ML models trained on this dataset, utilizing an array of text vectorization methods including TF-IDF, classical word and transformer-based Greek embeddings; (iii) a fine-tuned GREEK-BERT-based TC model on the same dataset; (iv) key empirical findings demonstrating that transformer-based embeddings significantly increase the performance of traditional ML models, while our fine-tuned DL model outperforms previous ones. The dataset, the best-performing model, and the experimental code are made public, aiming to augment the reproducibility of this work and advance future research in the field.

Does use of a hypothetical learning progression promote learning of the cardinal-count concept and give-n performance?

The general aim of the research was to conduct a rare test of the efficacy of hypothetical learning progressions (HLPs) and a basic assumption of basing instruction on HLPs, namely teaching each successive level is more efficacious than skipping lower levels and teaching the target level directly. The specific aim was evaluating whether counting-based cardinality concepts unfold in a stepwise manner. The research involved a pretest—delayed-posttest design with random assignment of 14 preschoolers to two conditions. The experimental intervention was based on an HLP for cardinality development (first promoting levels that presumably support and are necessary for the target level and then the target knowledge). The active-control treatment entailed a Teach-to-Target approach (first promoting irrelevant cardinality knowledge about recognizing written numbers and then directly teaching the same target-level goals with the same explicit instruction and similar games). A mix of quantitative and qualitative analyses indicated HLP participants performed significantly and substantially better than Teach-to-Target participants on target-level concept and skill measures. Moreover, the former tended to make sensible errors, whereas the latter generally responded cluelessly.

Integration of ChatGPT Into a Course for Medical Students: Explorative Study on Teaching Scenarios, Students' Perception, and Applications.

Text-generating artificial intelligence (AI) such as ChatGPT offers many opportunities and challenges in medical education. Acquiring practical skills necessary for using AI in a clinical context is crucial, especially for medical education. This explorative study aimed to investigate the feasibility of integrating ChatGPT into teaching units and to evaluate the course and the importance of AI-related competencies for medical students. Since a possible application of ChatGPT in the medical field could be the generation of information for patients, we further investigated how such information is perceived by students in terms of persuasiveness and quality. ChatGPT was integrated into 3 different teaching units of a blended learning course for medical students. Using a mixed methods approach, quantitative and qualitative data were collected. As baseline data, we assessed students' characteristics, including their openness to digital innovation. The students evaluated the integration of ChatGPT into the course and shared their thoughts regarding the future of text-generating AI in medical education. The course was evaluated based on the Kirkpatrick Model, with satisfaction, learning progress, and applicable knowledge considered as key assessment levels. In ChatGPT-integrating teaching units, students evaluated videos featuring information for patients regarding their persuasiveness on treatment expectations in a self-experience experiment and critically reviewed information for patients written using ChatGPT 3.5 based on different prompts. A total of 52 medical students participated in the study. The comprehensive evaluation of the course revealed elevated levels of satisfaction, learning progress, and applicability specifically in relation to the ChatGPT-integrating teaching units. Furthermore, all evaluation levels demonstrated an association with each other. Higher openness to digital innovation was associated with higher satisfaction and, to a lesser extent, with higher applicability. AI-related competencies in other courses of the medical curriculum were perceived as highly important by medical students. Qualitative analysis highlighted potential use cases of ChatGPT in teaching and learning. In ChatGPT-integrating teaching units, students rated information for patients generated using a basic ChatGPT prompt as "moderate" in terms of comprehensibility, patient safety, and the correct application of communication rules taught during the course. The students' ratings were considerably improved using an extended prompt. The same text, however, showed the smallest increase in treatment expectations when compared with information provided by humans (patient, clinician, and expert) via videos. This study offers valuable insights into integrating the development of AI competencies into a blended learning course. Integration of ChatGPT enhanced learning experiences for medical students.

Integrating Structured Digital Tools with the Traditional Hands-on Puzzle Method for Teaching Tooth Morphology: A Comparative Study of Educational Outcomes.

The study of tooth morphology is a critical component of the dental curriculum, highlighting the importance for dental students to acquire comprehensive and detailed knowledge of the complex structure of teeth. This study compared the educational outcomes of two student cohorts in a tooth morphology course, using traditional methods for the control group and additional digital video-based resources for the experimental group. We hypothesized that early integration of digital resources would significantly reduce the learning time. We retrospectively analyzed two groups of Master of Dentistry students. The control group (42 students) was taught using the traditional 'tooth puzzle' method, while the experimental group (42 students) supplemented traditional teaching with digital video-based tools developed by our department. Both groups' curricula culminated in a practical post-course test requiring the identification of 40 teeth, along with a mid-course test to track the students' learning progression. The number and type of incorrectly identified teeth were recorded. The mid-course test showed significant performance differences. The control group had a median (Q1, Q3) value of faults of 12.0 (7.8, 20.5), whereas the respective value for the experimental group was 4.0 (0.0, 8.0) (p < 0.001). In the control group, none achieved faultless results, with only two students (4.8%) having at most two faults, and six students (14.3%) having no more than four faults. The control group averaged 13.5 faults per student, with 19 students (45.2%) failing the test. Conversely, the experimental group showed improved performance: 12 students (28.6%) had no faults, and 25 students (59.5%) had four or fewer faults. The experimental group averaged 5.2 faults per student, with only four students (9.5%) failing. By the end of the course, both groups achieved commendable results on the practical tooth identification test. The experimental group slightly outperformed the control group, though the difference was not significant. The median (Q1, Q3) values were 0.0 (0.0, 2.5) and 1.0 (0.0, 4.5) for the experimental and control groups, respectively (p = 0.372). The students using both traditional and structured digital video-based tools showed greater learning advancement than those using only the traditional 'tooth puzzle' method.

A comparison of antibody-antigen complex sequence-to-structure prediction methods and their systematic biases.

The ability to accurately predict antibody-antigen complex structures from their sequences could greatly advance our understanding of the immune system and would aid in the development of novel antibody therapeutics. There have been considerable recent advancements in predicting protein-protein interactions (PPIs) fueled by progress in machine learning (ML). To understand the current state of the field, we compare six representative methods for predicting antibody-antigen complexes from sequence, including two deep learning approaches trained to predict PPIs in general (AlphaFold-Multimer and RoseTTAFold), two composite methods that initially predict antibody and antigen structures separately and dock them (using antibody-mode ClusPro), local refinement in Rosetta (SnugDock) of globally docked poses from ClusPro, and a pipeline combining homology modeling with rigid-body docking informed by ML-based epitope and paratope prediction (AbAdapt). We find that AlphaFold-Multimer outperformed other methods, although the absolute performance leaves considerable room for improvement. AlphaFold-Multimer models of lower quality display significant structural biases at the level of tertiary motifs (TERMs) toward having fewer structural matches in non-antibody-containing structures from the Protein Data Bank (PDB). Specifically, better models exhibit more common PDB-like TERMs at the antibody-antigen interface than worse ones. Importantly, the clear relationship between performance and the commonness of interfacial TERMs suggests that the scarcity of interfacial geometry data in the structural database may currently limit the application of ML to the prediction of antibody-antigen interactions.

Memory-Based Dynamic Bayesian Networks for Learner Modeling: Towards Early Prediction of Learners’ Performance in Computational Thinking

Artificial intelligence (AI) has demonstrated significant potential in addressing educational challenges in digital learning. Despite this potential, there are still concerns about the interpretability and trustworthiness of AI methods. Dynamic Bayesian networks (DBNs) not only provide interpretability and the ability to integrate data-driven insights with expert judgment for enhanced trustworthiness but also effectively process temporal dynamics and relationships in data, crucial for early predictive modeling tasks. This research introduces an approach for the temporal modeling of learners’ computational thinking abilities that incorporates higher-order influences of latent variables (hereafter referred to as memory of the model) and accordingly predicts learners’ performance early. Our findings on educational data from the AutoThinking game indicate that when using only first-order influences, our proposed model can predict learners’ performance early, with an 86% overall accuracy (i.e., time stamps 0, 5, and 9) and a 94% AUC (at the last time stamp) during cross-validation and 91% accuracy and 98% AUC (at the last time stamp) in a holdout test. The introduction of higher-order influences improves model accuracy in both cross-validation and holdout tests by roughly 4% and improves the AUC at timestamp 0 by roughly 2%. This suggests that integrating higher-order influences into a DBN not only potentially improves the model’s predictive accuracy during the cross-validation phase but also enhances its overall and time stamp-specific generalizability. DBNs with higher-order influences offer a trustworthy and interpretable tool for educators to foresee and support learning progression.

Identifying enthesitis in the sacroiliac joints in patients with axial spondyloarthritis by readers of varying experience: impact of the learning progress

BackgroundThis study aimed to investigate the accuracy of identifying enthesitis along with other inflammatory lesions and structural lesions on the MRI of the sacroiliac joints (SIJ) by readers of varying experience and how training sessions and workshops could help improve the accuracy.MethodsA total of 224 patients with clinical diagnosis of axial spondyloarthritis who underwent SIJ MRI examinations were retrospectively included in this study. Three readers with 5 years, 3 years and 1 year of experience in musculoskeletal imaging were invited to review the SIJ MRI images independently, while the imaging reports of a senior radiologist (> 10 years’ experience) were used as reference. After the first round of image review, a training session and a workshop on the imaging of SIJ in spondyloarthritis were held and the three readers were asked to review the images in the second round. We calculated the accuracy of identifying inflammatory and structural lesions of the three readers as well as the intra-reader agreement.ResultsEnthesitis could be observed in 52.23% of the axial spondyloarthritis patients, while 81.58% of the patients with enthesitis were accompanied with bone marrow edema. All the three readers showed better accuracy at identifying structural lesions than inflammatory lesions. In the first round of image review, the three readers only correctly identified 15.07%, 2.94% and 0.74% of the enthesitis sites. After the training session and workshop, the accuracy rose to 61.03%, 39.34% and 20.22%. The intra-reader agreement of enthesitis calculated as Cohen’s kappa was 0.23, 0.034 and 0.014, respectively.ConclusionReaders with less experience in musculoskeletal imaging showed lower accuracy of identifying inflammatory lesions, notably enthesitis. Training sessions and workshops could help improve the diagnostic accuracy of the junior readers.

AI-POWERED IMAGE ENHANCEMENT IN FORENSIC APPLICATIONS: CHALLENGES AND OPPORTUNITIES

Recent progress in machine learning (ML) and computer vision has markedly enhanced the performance of automated facial recognition systems. Nonetheless, forensic applications frequently face issues with poor-quality and low-resolution images that pose challenges even to the most advanced facial recognition technologies. This research explores the potential of neural-based image enhancement and restoration techniques to recover degraded images while maintaining original correspondences for legal use. We evaluate super-resolution and deconvolution methods across two comprehensive and varied facial datasets. Our study employs twelve distinct GAN- and diffusion-based enhancement techniques. Our results offer insights and recommendations for the effective application of image enhancement in forensic facial recognition, highlighting the advantages and potential limitations of these advanced technologies.