arXiv subjects

Samar Shailendra

Publications and source records attributed to Samar Shailendra.

16 recordsLinked to original sources

arXiv2026

The 5P Reflection Model for Education in the Generative Artificial Intelligence (GenAI) Era

Contributions: A reflection model suitable for the era of Generative Artificial Intelligence (GenAI) is introduced. The proposed model is an integrated model that extracts features from various existing models and also incorporates technological aspects of GenAI. Background: Universities worldwide are facing challenges in adopting GenAI into their curricula, as it has impacted academic integrity and the scholarship of teaching and research. Traditional reflection models are struggling to authenticate student reflection as GenAI is incorporated in education. This requires a GenAI-aware model to enable the opportunities that address the associated challenges with GenAI. Research Questions: Does the academic ecosystem require a GenAI-aware reflection model to adopt GenAI into education? How to make a reflection model structured to ensure student authenticity and cognitive engagement within a GenAI-aware learning environment? Methodology: This study employs a design-based research methodology, assisted by a critical inquiry approach, to analyse existing reflection models in the era of GenAI and examine the technological aspects of GenAI. Additionally, it identifies a gap and the absence of a comprehensive reflection model that enhances reflection in the era of GenAI and supports the effective integration of GenAI in education. Findings: The literature survey indicates there is a greater need for reflection in the era of GenAI to ensure learning, but the existing reflection models lack proper strategies to manage the problem introduced by GenAI. A standard GenAI-aware reflection model, called 5P (Purpose, Process, Product, Pitfalls and Plan), is proposed i) to manage greater demand for reflection, ii) to address the limitations of the existing reflection models, iii) to consider technological aspects of GenAI.

cs.CY↗

arXiv2026

GRASP: Graph-Retrieval Automated Scoring Pipeline for Label-Free Multi-Topic Essay Grading

Automated short-answer grading research has historically focused on exams consisting solely of questions pertaining to a single topic. Automatic grading of exams containing questions about more than one topic remains less explored. In this work, a Graph-Retrieval Automated Scoring Pipeline (GRASP) is introduced for grading label-free multi-topic science exams. Label-free exams are short-answer exams in which a student's responses to several distinct topics are merged into a single paragraph, with no markup labels or segmentation indicating which span answers which question. Reference answers for each question are encoded into a FAISS vector index via Sentence-BERT, and a semantic similarity graph is constructed over this set of reference answers. At grading time, sentence count heuristics, with a large language model used to resolve ambiguous cases, are first applied to predict how many distinct topics were answered in the student essay. This process is performed without training data or domain-specific example essays. Candidate reference nodes, each storing one (question, reference answer, concatenation of both) from the reference index, are then retrieved through cosine similarity based Retrieval-Augmented Generation (RAG) and Graph Retrieval-Augmented Generation (GRAG). GRAG operates by taking the top cosine matches as seed nodes and then performing a graph traversal over strong edges to find additional reference nodes that may have been missed by RAG. The Hungarian algorithm is then used to optimally assign one reference node per question segment such that no reference is duplicated. Each segment is then graded against its assigned reference independently using GPT-4.1-mini. This experiment is performed to show the effect of retrieval quality on grading accuracy and the benefit of graph-augmented retrieval versus strict cosine similarity methods at various levels of essay complexity.

cs.IR↗

arXiv2026

Dynamic Evidence Collection Ecosystem for Assessment Integrity and Authentic Competence

Generative Artificial Intelligence (GenAI) can produce high-quality essays, code, and design artefacts, challenging the validity of conventional assessments that rely on single-point submissions and product-only grading. This paper proposes a design framework called "Dynamic Evidence Collection Ecosystem" that shifts assessment toward continuous, authentic, multi-source evidence of student learning over time. The framework collects process evidence through iterative artefacts, design logs, activity rounds, self-reflection, and peer collaboration, supported by an AI-enabled layer for learning analytics, formative feedback, and transparency. The approach is grounded in recent assessment-redesign scholarship in AI-rich contexts and aligned with contemporary views of authenticity in assessment. This paper builds on the hypothesis that academic integrity is strengthened when it is treated as an assessment design rather than as an AI detection problem. The tools have limitations and risks of use that carry academic penalties. This paper presents an implementation scenario to support institutional adoption.

cs.HC↗

arXiv2026

Religion and Artificial Intelligence as Distributed Meaning Systems: A Naturalistic Conceptual Model

This paper develops a naturalistic account of religion and artificial intelligence as structurally similar distributed meaning systems. I argue that both emerge from the same underlying cognitive architecture: socially extended processes that offload interpretation, norm-guidance, and world-model construction into external symbolic environments. Drawing on work in distributed cognition, cultural evolution, and philosophy of mind, the paper proposes a conceptual model showing how meaning is generated, stabilised, and transmitted through recursive interactions between agents and their informational ecologies. Religion is analysed not as a set of beliefs but as a cognitive-ecological system that scaffolds coordination, normativity, and shared interpretation. Contemporary AI systems are shown to instantiate analogous functions, operating as high-bandwidth, algorithmically mediated environments that shape reasoning, attention, and social meaning-making. The model explains how both systems create epistemic compression, reduce cognitive load, and generate shared frameworks that guide behaviour. It also clarifies the conditions under which AI systems can become culturally entrenched meaning authorities. The contribution is conceptual: a unified framework for understanding religion and AI as parallel forms of distributed cognitive machinery. This reframing opens new pathways for analysing artificial agents not as isolated tools but as components in evolving socio-cognitive ecologies.

cs.CY↗

arXiv2026

The Digital Pirah\~a Condition: Ecological Mismatch and the Reconstruction of Recursive Cognition

Contemporary digital and AI-mediated environments are reshaping the cognitive ecologies within which human reasoning develops. As everyday activity becomes embedded in datafied infrastructures, cognitive habits adapt to conditions of immediacy, fragmentation, externalisation, and algorithmic filtering. This paper introduces the Digital Pirah\~a Condition, a cultural ecological model explaining how these environments cultivate adaptive but shallow cognitive patterns, epistemic flattening, reduced recursive capacity, and heightened reliance on external scaffolds. While functional within digital systems, these adaptations create an ecological mismatch with the recursive, integrative reasoning required in academic and institutional activity systems. The paper argues that this mismatch is an ecological outcome rather than a psychological deficit, and that addressing it requires intentional cognitive niche construction within educational institutions. The lecturer is conceptualised as a cultural entrepreneur who reconstructs the cognitive ecology of learning through analog sanctuaries, AI-supported metacognitive scaffolds, and recursive curriculum architectures. The Digital Pirah\~a Condition thus provides a theoretical lens for understanding contemporary cognitive change and a framework for ecological redesign in AI-mediated societies.

cs.HC↗

arXiv2026

CLIF: Cross-layer LEO-ISL Fingerprinting for Physical and Network Attack Detection in Dense LEO Constellations

Low-Earth Orbit (LEO) mega-constellations such as Starlink by SpaceX and Kuiper by Amazon rely on optical Inter-Satellite Links (ISLs) for autonomous mesh routing to provide low-latency telecommunication, Internet of Things (IoT), and security services globally. As commercial operators and governments deploy increasingly dense constellations and form multi-operator peering coalitions, ISL integrity becomes critical to both commercial availability and national security. However, there is a lack of real-world data for LEO constellations and existing real-time security approaches focus strictly on physical layer security, leaving blind spots in the coverage of network-layer and composite attacks. In this paper, we present a cross-layer, lightweight behavioral fingerprinting framework that fuses onboard physical-layer measurements with network-layer data to detect anomalies at low computational overhead. We construct an orbital simulation covering the first shells of Starlink (1,584 satellites), Kuiper (1,156 satellites), and a joint multi-operator peering scenario (2,740 satellites), injecting ten attack types that span spoofing, traffic manipulation, and routing subversion at varying severity. We evaluate three unsupervised, per-satellite detectors among which our Mahalanobis-distance-based detector achieves 99.5% recall on Starlink, 99.4% on Kuiper, and 94.8\% on the multi-operator constellation, while maintaining False Positive Rates (FPR) below 0.7%. Our results demonstrate that cross-layer feature fusion is not only necessary for comprehensive security of LEO constellations but highly cost-effective for large-scale networks while fitting into the strict onboard energy budgets of resource-constrained satellites.

cs.CR↗

arXiv2026

L-PRISMA: An Extension of PRISMA in the Era of Generative Artificial Intelligence (GenAI)

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework provides a rigorous foundation for evidence synthesis, yet the manual processes of data extraction and literature screening remain time-consuming and restrictive. Recent advances in Generative Artificial Intelligence (GenAI), particularly large language models (LLMs), offer opportunities to automate and scale these tasks, thereby improving time and efficiency. However, reproducibility, transparency, and auditability, the core PRISMA principles, are being challenged by the inherent non-determinism of LLMs and the risks of hallucination and bias amplification. To address these limitations, this study integrates human-led synthesis with a GenAI-assisted statistical pre-screening step. Human oversight ensures scientific validity and transparency, while the deterministic nature of the statistical layer enhances reproducibility. The proposed approach systematically enhances PRISMA guidelines, providing a responsible pathway for incorporating GenAI into systematic review workflows.

cs.DL↗

arXiv2025

Navigating the New Landscape: A Conceptual Model for Project-Based Assessment (PBA) in the Age of GenAI

The rapid integration of Generative Artificial Intelligence (GenAI) into higher education presents both opportunities and challenges for assessment design, particularly within Project-Based Assessment (PBA) contexts. Traditional assessment methods often emphasise the final product in the PBA, which can now be significantly influenced or created by GenAI tools, raising concerns regarding product authenticity, academic integrity, and learning validation. This paper advocates for a reimagined assessment model for Project-Based Learning (PBL) or a capstone project that prioritises process-oriented evaluation, multi-modal and multifaceted assessment design, and ethical engagement with GenAI to enable higher-order thinking. The model also emphasises the use of (GenAI-assisted) personalised feedback by a supervisor as an observance of the learning process during the project lifecycle. A use case scenario is provided to illustrate the application of the model in a capstone project setting. The paper concludes with recommendations for educators and curriculum designers to ensure that assessment practices remain robust, learner-centric, and integrity-driven in the evolving landscape of GenAI.

cs.CY↗

arXiv2025

Experiences with Content Development and Assessment Design in the Era of GenAI

Generative Artificial Intelligence (GenAI) has the potential to transform higher education by generating human-like content. The advancement in GenAI has revolutionised several aspects of education, especially subject and assessment design. In this era, it is crucial to design assessments that challenge students and cannot be solved using GenAI tools. This makes it necessary to update the educational content with rapidly evolving technology. The assessment plays a significant role in ensuring the students learning, as it encourages students to engage actively, leading to the achievement of learning outcomes. The paper intends to determine how effectively GenAI can design a subject, including lectures, labs and assessments, using prompts and custom-based training. This paper aims to elucidate the direction to educators so they can leverage GenAI to create subject content. Additionally, we provided our experiential learning for educators to develop content, highlighting the importance of prompts and fine-tuning to ensure output quality. It has also been observed that expert evaluation is essential for assessing the quality of GenAI-generated materials throughout the content generation process.

cs.CY↗

arXiv2024

Framework for Adoption of Generative Artificial Intelligence (GenAI) in Education

Contributions: An adoption framework to include GenAI in the university curriculum. It identifies and highlights the role of different stakeholders (university management, students, staff, etc.) during the adoption process. It also proposes an objective approach based upon an evaluation matrix to assess the success and outcome of the GenAI adoption. Background: Universities worldwide are debating and struggling with the adoption of GenAI in their curriculum. Both the faculty and students are unsure about the approach in the absence of clear guidelines through the administration and regulators. This requires an established framework to define a process and articulate the roles and responsibilities of each stakeholder involved. Research Questions: Whether the academic ecosystem requires a methodology to adopt GenAI into its curriculum? A systematic approach for the academic staff to ensure the students' learning outcomes are met with the adoption of GenAI. How to measure and communicate the adoption of GenAI in the university setup? Methodology: The methodology employed in this study focuses on examining the university education system and assessing the opportunities and challenges related to incorporating GenAI in teaching and learning. Additionally, it identifies a gap and the absence of a comprehensive framework that obstructs the effective integration of GenAI within the academic environment. Findings: The literature survey results indicate the limited or no adoption of GenAI by the university, which further reflects the dilemma in the minds of different stakeholders. For the successful adoption of GenAI, a standard framework is proposed i) for effective redesign of the course curriculum, ii) for enabling staff and students, iii) to define an evaluation matrix to measure the effectiveness and success of the adoption process.

cs.CY↗

arXiv2024

Crafting Tomorrow's Evaluations: Assessment Design Strategies in the Era of Generative AI

GenAI has gained the attention of a myriad of users in almost every profession. Its advancement has had an intense impact on education, significantly disrupting the assessment design and evaluation methodologies. Despite the potential benefits and possibilities of GenAI in the education sector, there are several concerns primarily centred around academic integrity, authenticity, equity of access, assessment evaluation methodology, and feedback. Consequently, academia is encountering challenges in assessment design that are essential to retaining academic integrity in the age of GenAI. In this article, we discuss the challenges, and opportunities that need to be addressed for the assessment design and evaluation. The article also highlights the importance of clear policy about the usage of GenAI in completing assessment tasks, and also in design approaches to ensure academic integrity and subject learning. Additionally, this article also provides assessment categorisation based on the use of GenAI to cultivate knowledge among students and academic professionals. It also provides information on the skills necessary to formulate and articulate problems and evaluate the task, enabling students and academics to effectively utilise GenAI tools.

cs.CY↗

arXiv2018

Internet of Things: Technology, Applications and Standardardization

The term "Internet of Things" (IoT) refers to an ecosystem of interconnected physical objects and devices that are accessible through the Internet and can communicate with each other. The main strength of the IoT vision is the high impact it has created and will continue to do so on several aspects of the everyday life and behavior of its potential users. This book presents some of the state-of-the-art research work in the field of the IoT, especially on the issues of communication protocols, interoperability of protocols and semantics, trust security and privacy issues, reference architecture design, and standardization. It will be a valuable source of knowledge for researchers, engineers, practitioners, and graduate and doctoral students who are working in various fields of the IoT. It will also be useful for faculty members of graduate schools and universities.

cs.NI↗

arXiv2017

Analysis of Analog Network Coding noise in Multiuser Cooperative Relaying for Spatially Correlated Environment

Analog Network Coding (ANC) is proposed in literature to improve the network throughput by exploiting channel diversity. In practical scenarios, due to the difference in channel characteristics, an extra residual component, termed as ANC noise, appears during the processing of the received signal. This ANC noise component may suppress the ANC gain. None of the existing literature to our knowledge considers the effect of spatial correlation among channels on ANC noise. This paper develops a generic framework to investigate the effect of channel characteristics on ANC noise. We have modelled the channels as spatially correlated to take the (dis)similarity among them into account. Per node power constraint is also taken into consideration. In this work, we have characterized the behaviour of ANC noise and presented the results to analyze the network performance in terms of outage probability. Outcomes of our investigation show that spatial correlation among channels significantly affects the variance of ANC noise as well as the outage performance of the system. The proposed framework can provide better insights while selecting the system parameters in a correlated environment.

cs.NI↗

arXiv2017

Enhanced Socket API for MPTCP - Controlling Sub-flow Priority

Multipath TCP (MPTCP) can exploit multiple available interfaces at the end devices by establishing concurrent multiple connections between source and destination. MPTCP is a drop-in replacement for TCP and this makes it an attractive choice for various applications. In recent times, MPTCP is finding its way into newer devices such as robots and Unmanned Aerial Vehicles (UAVs). However, its usability is often restricted due to unavailability of suitable socket APIs to control its behaviour at the application layer. In this paper, we have introduced several socket APIs to control the sub-flow properties of MPTCP at the application layer. We have proposed a modification in MPTCP kernel data-structure to make the sub-flow priority persistent across sub-flow failures. We have also presented Primary Path only Scheduler (PPoS), a novel sub-flow scheduler, for UAVs and similar applications/devices where it is necessary to segregate data on different links based upon type of data or Quality of Service (QoS) requirements. We have also introduced the socket APIs for providing the fine grained control over the behaviour of PPoS for particular application(s) rather than changing the behaviour system wide. The scheduler and the socket APIs are extensively tested in Mininet based emulation environment as well as on real Raspberry Pi based testbed.

cs.NI↗

arXiv2016

Improving congestion control for Concurrent Multipath Transfer through bandwidth estimation based resource pooling

Stream Control Transmission Protocol (SCTP) was introduced in 2001 as a multipath variant to traditional transport protocols, i.e. Transmission Control Protocol (TCP) and User Datagram Protocol (UDP). Concurrent Multipath Transfer (CMT) has been proposed as an extension for SCTP to support concurrent usage of available multiple paths. In this paper, we propose a new congestion control algorithm for CMT-SCTP based on the principle of resource pooling. We use the connection bandwidth estimates to obtain the collection of the network resources being used by different flows on multiple paths. Based on these bandwidth estimates, we have used the bandwidth estimation based resource pooling approach to adjust the congestion window of the respective paths. We compare our proposed scheme with CMT-SCTP through ns-2 based simulations.

cs.NI↗

arXiv2016

Performance Evaluation of Caching Policies in NDN - an ICN Architecture

Information Centric Networking (ICN) advocates the philosophy of accessing the content independent of its location. Owing to this location independence in ICN, the routers en-route can be enabled to cache the content to serve the future requests for the same content locally. Several ICN architectures have been proposed in the literature along with various caching algorithms for caching and cache replacement at the routers en-route. The aim of this paper is to critically evaluate various caching policies using Named Data Networking (NDN), an ICN architecture proposed in literature. We have presented the performance comparison of different caching policies naming First In First Out (FIFO), Least Recently Used (LRU), and Universal Caching (UC) in two network models; Watts-Strogatz (WS) model (suitable for dense short link networks such as sensor networks) and Sprint topology (better suited for large Internet Service Provider (ISP) networks) using ndnSIM, an ns3 based discrete event simulator for NDN architecture. Our results indicate that UC outperforms other caching policies such as LRU and FIFO and makes UC a better alternative for both sensor networks and ISP networks.

cs.NI↗