Review Article

Understanding Research Novelty and Research Gaps: A Systematic Conceptual Review and Technology-Driven Strategies for Identification

Faridah Mohd Said1*, Etik Pratiwi1,2

¹School of Nursing and Applied Health Science, Lincoln University College, Wisma Lincoln, No 12-18, Jalan SS 6/12, 47301, Petaling Jaya, Selangor Darul Ehsan, Malaysia

²Département of Nursing, Sekolah Tinggi Ilmu Kesehatan Notokusumo Yogyakarta,Jl. Bener No 26 Tegalrejo, Yogyakarta, 55243 Indonesia

Corresponding Author’s Email: faridah.msaid@lincoln.edu.my

Abstract

Research novelty and gap identification are critical drivers of scientific progress, enabling scholars to move beyond incremental contributions toward transformative discoveries. Novelty refers to original concepts, perspectives, or methodologies, while research gaps denote unresolved or underexplored areas within existing knowledge. This review followed the SPIDER framework (Sample, Phenomenon of Interest, Design, Evaluation, Research Type) to structure the search strategy. The guiding question was: “What are the conceptualizations, typologies, and identifying methodologies for ‘research novelty’ and ‘research gaps’ across various designs, and how have these been qualitatively explored?”. Comprehensive searches were performed across Google Scholar, PubMed/NCBI, Scopus, DOAJ, and ERIC. From 1,250 screened records, 10 seminal studies (2022–2025) were included based on relevance and quality. The protocol was registered with PROSPERO CRD420251178180. Findings reveal that interdisciplinary collaboration and AI-powered approaches—deep learning models and human–GenAI frameworks—are revolutionizing how novelty is measured and gaps are mapped. These advanced methods outperform citation-based metrics by autonomously detecting latent patterns in large scholarly datasets, offering precision and scalability. By integrating technology-driven strategies with conceptual rigor, this study provides actionable insights for researcher, and policymakers to strategically position research for maximum impact. Furthermore, performative collaboration, characterized by a lack of genuine integration, proves less effective than shared protocols for accurately predicting novelty. Future research should focus on developing hybrid models that combine semantic metrics with reinforcement learning approaches. To advance these goals, researchers should leverage AI for ideation, funders must prioritize interdisciplinary AI-assisted proposals, and developers are tasked with building transparent, auditable systems.

Keywords: Artificial Intelligence; Deep Learning; Interdisciplinary Research; Research Gaps; Research Novelty

Introduction

Advancing scientific knowledge depends on two critical elements: novelty in research and the identification of research gaps (Okeke, 2025). However, researchers frequently encounter two interrelated challenges: articulating the novelty of their work and identifying meaningful research gaps within the literature (Baako et al., 2022). Novelty refers to the introduction of original concepts, methods, or perspectives that extend beyond established paradigms, while research gaps represent areas of incomplete understanding or unresolved questions within existing literature. Together, these elements ensure that scholarly work contributes meaningfully rather than incrementally (Tennant et al., 2017). Their significance extends beyond academia novel research drives technological innovation, informs evidence-based policy, and strengthens institutional and national competitiveness (Salter & Martin, 2001). In an era of rapid knowledge expansion and digital transformation, systematically identifying novelty and gaps has become increasingly complex, requiring robust conceptual frameworks and advanced analytical tools (Shin et al., 2022). This review addresses these challenges by synthesizing recent methodological developments and exploring technology-driven strategies for precision and scalability in novelty detection (Cohen et al., 2002).

Novelty Claim of This Review

The novelty of this review lies in its integrative and forward-looking synthesis of interdisciplinary and AI- driven approaches to identifying research gaps and research novelty (Huang et al., 2026). Unlike prior conceptual discussions that rely predominantly on citation-based indicators or narrative judgment, this review consolidates emerging evidence demonstrating how deep learning models, topic modeling, novelty-driven reinforcement learning, and collaboration between humans and GenAI frameworks can systematically detect latent intellectual spaces across disciplines (Wang et al., 2024). By bridging methodological scholarship with technological innovation, this article advances a scalable, data- informed framework that redefines how novelty is conceptualized and operationalized, thereby offering a substantive contribution to research methodology literature. Research novelty, as a formal evaluation criterion, prompted a shift in top priorities toward emerging, underexplored areas—such as the convergence of gaming and gambling, technological innovations, and treatment process barriers— rather than the replication of established knowledge or the testing of new interventions (Czakó et al., 2025).

Practical Significance

The ability to identify novelty and research gaps carries profound implications for both academic and societal progress.

First, funding and resource allocation are strongly influenced by originality; novel research often attracts competitive grants and institutional investment, positioning scholars, and institutions at the forefront of discovery (Cohen et al., 2002).

Second, policy development benefits from systematic gap identification, as addressing knowledge deficits informs strategic decisions in critical domains such as healthcare, education, and technology (Salter & Martin, 2001)

Third, career advancement is closely tied to originality; demonstrating novelty enhances scholarly reputation, visibility, and opportunities for collaboration (Okeke, 2025).

Fourth, innovation and industry impact accelerate when research introduces novel findings, enabling faster product development and solutions to societal challenges (Salter & Martin, 2001)

Finally, global competitiveness hinges on prioritizing novelty-driven research, as nations that invest in originality strengthen their knowledge economies and innovation ecosystems (Cohen et al., 2002).

Objectives

This study aims to:

1. Clarify conceptual distinctions between novelty in research and research gaps.

2. Survey typologies and methodologies for identifying these elements across disciplines.

3. Articulate the relationship between gaps and originality to guide researchers in positioning their contributions strategically.

Methodology

Study Design and Protocol

This study is literature review was conducted using the SPIDER framework (Sample, Phenomenon of Interest, Design, Evaluation, Research type) to structure the search strategy and eligibility criteria (Yang et al., 2026).

Identify the Research Question

Researchers prepared research questions using the SPIDER scheme (Sample, Phenomenon of Interest, Design, Evaluation, and Research Type). This format is attached in Table 1. The guiding research question in this systematic review is “What are the conceptualizations, typologies, and identifying methodologies for 'research novelty' and 'research gaps' as presented in the scholarly methodological literature of various designs, and how have these been qualitatively explored?".

Table 1: Spider Format

Figure 1 illustrates the conceptual framework of this review, demonstrating how multidisciplinary literature is systematically analyzed using artificial intelligence and deep learning techniques to identify research gaps and articulate research novelty. Interdisciplinary collaboration and human–AI interaction serve as enabling mechanisms that transform fragmented knowledge into high-impact scholarly contributions.

Figure 1: AI-Driven Conceptual Framework for Identifying Research Gaps and Research Novelty

Searching Literature

Comprehensive searches were performed across Google Scholar, PubMed/NCBI, Scopus, DOAJ, and ERIC, targeting conceptual and methodological literature on novelty and gap identification. From 1,250 screened records, 10 seminal studies published between 2022 and 2025 were included based on relevance and quality as shown in Figure 2. The review protocol was registered with PROSPERO (CRD420251178180) to ensure transparency and rigor (Okeke, 2025).

Figure 2: PRISMA Flow Diagram Illustrating the Identification, Screening, Eligibility, and Inclusion Stages

Data extraction

In data extraction of the article review format, the author entered it into an electronic spreadsheet, so that the data extraction process could be managed well. The data extraction process consists of: (1) author and title, (2) researcher's country of origin, (3) objectives, (4) methods, (5) results, (6) implications.

Results

The reviewed literature reveals distinct, country-specific findings regarding the role of novelty across scientific, cognitive, and technological domains (Cochrane Central Editorial Service et al., 1996) Table 2 presents the data extracted from the research summaries, including the author, title, author's country of origin, objectives, methods, results, and implications. In Ireland, studies demonstrated a significant negative relationship between novelty and both scientific collaboration and funding, indicating that funded papers exhibited lower novelty than unfunded ones. Dutch research identified novelty as a "cognitive highlighter" that enhances memory consolidation within specific time windows; additionally, Dutch scholarship provided a comprehensive review of Building Information Modelling (BIM) and sustainability. Chinese contributions encompassed the development of a quantitative framework (ITMCM) for measuring scientific novelty and the ERLNEIL-MDP algorithm, which established new benchmarks for medical data processing. Furthermore, Chinese research provided evidence that human– Gen AI collaboration specifically boosts creative novelty during the initial idea-generation phase. From other national contexts, a novel hybrid approach in Bangladesh achieved high accuracy in sentiment analysis. Malaysian research demonstrated that unsupervised deep learning effectively detects novelties in time-series data. Mexican scholarship concluded that AI in medical education has transitioned from a novelty to a pedagogical necessity. Finally, Nigerian research offered strategies to identify uncharted study areas by clarifying the distinction between research novelty and existing research gaps.

Table 2: Article Review Format

Three thematic clusters emerged from the synthesis. The pursuit of novelty is a defining aspiration of scholarly work, yet enabling and sustaining it across disciplines remains challenging. Recent scholarship indicates that novelty emerges from the orchestration of diverse teams, methodological pluralism, and intelligent systems capable of surfacing overlooked patterns and ideas (Shin et al., 2022). Synthesizing current developments, three thematic clusters illuminate complementary pathways to novelty: (1) interdisciplinary collaboration and knowledge recombination, (2) advanced measurement frameworks beyond citation-based proxies, and (3) AI-enabled identification and generation of novel insights (Wang et al., 2024; Jeon et al., 2023; Islam et al., 2024; Lv et al., 2024; Huang et al., 2026).

Interdisciplinary Collaboration and Knowledge Recombination

Interdisciplinary collaboration is a fertile ground for innovation, particularly when teams span epistemic communities with distinct methods, vocabularies, and problem framings. Diverse teams are positioned to create novel conceptual linkages and methodological syntheses, enabling knowledge recombination that escapes domain constrained thinking (Shin et al., 2022). Organizational routines cross disciplinary seminars, shared lab spaces, co-supervision, and joint grants cultivate conditions for serendipitous alignment, while leadership interventions such as recognition of team-based outputs and seed funding help convert friction into productive tension that fuels originality (Shin et al., 2022). For postgraduate training, mixed-methods cohorts and translational problem statements equip early-stage researchers to navigate conceptual pluralism and articulate novel hypotheses (Mohammadi et al., 2025).

Measurement Frameworks Beyond Citation-Based Proxies

Citation counts are blunt instruments for capturing novelty because they lag-behind conceptual breakthroughs and conflate popularity with originality. Content-based analytics provide a more granular lens. Topic modeling approaches such as BER Topic, leveraging transformer embeddings to derive semantically coherent topics, allow researchers to track micro-shifts in discourse and identify emergent clusters deviating from mainstream trajectories (Wang et al., 2024). When combined with probabilistic cloud models, these methods yield nuanced portraits of conceptual landscapes, capturing ambiguity and dispersion typical of nascent ideas (Wang et al., 2024). Text-embedding outlier detection locates papers whose semantic vectors sit at the margins of established clusters; systematic analysis coupled with expert review can reveal underappreciated innovations (Jeon et al., 2023). Multidimensional scoring, structural, methodological, and contextual novelty, prevents overfitting novelty to a single definition (Steyn, 2025).

AI-Enabled Identification and Generation of Novel Insights

Novelty detection is transforming from manual, expert-driven processes to scalable AI-enabled systems (Lv et al., 2024). Deep learning models trained on large corpora detect subtle semantic patterns, propose connections across literatures, and flag hypothesis spaces that warrant exploration (Islam et al., 2024). Reinforcement learning frameworks that incorporate novelty-driven exploration reward agents for venturing into less traversed conceptual territories, mitigating exploitation bias common in recommendation systems (Lv et al., 2024). Human–GenAI collaboration is a pragmatic equilibrium: AI surfaces candidate ideas and gaps, while human experts evaluate plausibility, relevance, and ethics (Huang et al., 2026). Guardrails and transparency in criteria, domain-bias audits, and human-in-the- loop validation to ensure AI-enabled novelty serves scholarly integrity (Islam et al., 2024; Huang et al., 2026).

Integrating the Clusters: A Conceptual Pathway

These clusters are mutually reinforcing collaboration provides the social and cognitive infrastructure for recombination; measurement frameworks articulate novelty in interpretable, multidimensional ways; and AI systems operationalize detection at scale, shortening the time from idea emergence to recognition and evaluation (Shin et al., 2022; Wang et al., 2024; Islam et al., 2024). Figure 3 indicates the novelty identification pipeline. Interdisciplinary teams generate candidate ideas, which are scored and visualized through content-based analytics. AI agents scan for underexplored connections and propose expansions, followed by human expert refinement and validation. Measurement updates inform future team composition and resource allocation (Jeon et al., 2023; Huang et al., 2026).

Figure 3: The Conceptual Pathway of Novelty Identification Pipeline

Discussion

The present systematic conceptual review synthesized recent scholarship on research novelty and research gaps, revealing three complementary thematic clusters: (1) interdisciplinary collaboration and knowledge recombination, (2) advanced measurement frameworks beyond citation-based proxies, and

(3) AI-enabled identification and generation of novel insights. Collectively, these findings shift the discussion on novelty from an abstract aspiration to actionable, technology-driven strategies.

The first cluster confirms novelty rarely emerges from isolated, single-discipline efforts. Instead, it thrives in teams crossing epistemic boundaries, integrating diverse methods, vocabularies, and problem framings (Chen et al., 2025). Crucially, assembling diverse experts alone is insufficient; intentional organizational routines—like joint grant applications, shared lab spaces, and co-supervision—are essential for transforming potential friction into productive tension (Liu et al., 2022). This aligns with recent calls to redesign academic incentive structures, rewarding team-based outputs over solo authorship (Gasparetto et al., 2025). In training environments, mixed-methods cohorts and translational problem statements prepare early-stage researchers to navigate conceptual pluralism and articulate novel hypotheses often unseen within a single discipline (James et al., 2024).

Traditional novelty assessment is significantly limited by an over-reliance on citation counts, which are retrospective, slow to accrue, and conflate popularity with originality (Haynes-Brown, 2025). This section highlights emerging content-based analytics that offer a more granular and immediate perspective (Hou et al., 2023). Transformer based topic modelling (BER Topic), combined with probabilistic cloud models, can capture semantic coherence and identify emergent research clusters that deviate from mainstream trajectories. Similarly (Jeong et al., 2024) utilized text embedding outlier detection to locate papers whose semantic vectors are situated at the periphery of established clusters, thereby flagging potentially underappreciated innovations for systematic review (Gogo, 2022). These methods move beyond binary (novel vs. not) judgments toward multidimensional scoring—structural, methodological, and contextual novelty—preventing overfitting to a single definition (Del Castillo & Guerguerian, 2026). For research gap identification, these tools systematically reveal underrepresented topics, unreplicated findings, or contradictory evidence that have eluded collective attention (Cochrane Central Editorial Service et al., 2025).

This third cluster represents a paradigm shift in novelty detection, transitioning from manual, expert- driven processes to scalable, AI-enabled systems (Michael Knop et al., 2024). Deep learning models trained on extensive corpora can discern subtle semantic patterns and posit connections across disparate literatures (Palumbo et al., 2026). Furthermore, advanced reinforcement learning frameworks that utilize novelty-driven exploration reward agents for venturing into less-traversed conceptual territory, thereby mitigating the exploitation bias common in recommendation systems (Muna, 2025). Nonetheless, the review also underscores several critical caveats: AI systems possess the capacity to entrench existing biases or generate superficially novel but implausible or unethical ideas (Lopez, 2025). Consequently, a pragmatic equilibrium necessitates human-GenAI collaboration, wherein AI identifies candidate ideas and potential gaps, and human experts critically evaluate their plausibility, relevance, and ethical implications (Jusoh et al.,2025; Leung et al., 2025) Robust safeguards, comprehensive domain-bias audits, and transparent evaluative criteria are thus paramount to ensure that AI-enabled novelty genuinely contributes to scholarly integrity, rather than inadvertently compromising it (Cochrane Central Editorial Service et al., 1996) .

A core argument of this review is that the three clusters are not independent but mutually reinforcing (El Arab et al., 2025). Specifically, interdisciplinary collaboration builds the social and cognitive infrastructure for knowledge recombination (Flindris et al., 2025). Advanced measurement frameworks then articulate novelty through interpretable, multidimensional analyses. Finally, AI systems operationalize detection at scale, significantly shortening the time from idea emergence to recognition (Yardibi et al., 2025).

Figure 3 illustrates how an integrated pipeline could operate: First, interdisciplinary teams generate candidate ideas. These ideas are then scored and visualized using content-based analytics. Next, AI agents scan for underexplored connections and propose hypothesis expansions, which human experts then refine and validate (Schlicht et al., 2025). Finally, measurement feedback continuously informs future team composition and resource allocation. This closed-loop system transforms novelty from an elusive ideal into a manageable, transparent, and scalable objective (Quistberg et al., 2025).

The findings highlight a paradigm shift from traditional citation-based measures toward AI-driven, content-centric approaches (Jeon et al., 2023). Measurement frameworks that integrate topic modeling and anomaly detection yield earlier and more granular signals of originality (Wang et al., 2024). Human– GenAI collaboration should be aligned with creative-phase goals to maximize novelty in ideation and usefulness in elaboration (Huang et al., 2026). Interdisciplinary collaboration remains a cornerstone but requires attention to funding structures and team composition (Shin et al., 2022). Ethical considerations algorithmic bias, transparency, interpretability, and risk of metric gaming—must guide adoption to ensure responsible evaluation (Akbari et al., 2024).

Similarity and Integrity

This manuscript underwent an internal similarity and redundancy check using local tools. No external similarity services were used in this draft. Prior to journal submission, the manuscript will be screened using Turnitin/iThenticate, and a similarity certificate will be provided to ensure compliance with publication ethics.

Limitations and Future Directions

Novelty is field-contingent; definitions and baselines vary across disciplines, requiring domain calibration and expert input (Wang et al., 2024). AI systems inherit training-data biases that can marginalize non-English or regional scholarship; diverse corpora and regular bias audits are essential (Islam et al., 2024; Lv et al., 2024). Collaboration can be performative without genuine methodological integration; co-developed protocols and shared standards are more predictive of novelty than nominal interdisciplinarity (Shin et al., 2022; Ni & Wang, 2025). Future work should pursue hybrid models that combine semantic metrics with experimental validation and RL frameworks that reward relevance, robustness, and equity (Lv et al., 2024; Huang et al., 2026).

For individual researchers and trainees, the findings suggest several actionable steps: participate in mixed-methods cohorts, leverage topic modeling tools to delineate the periphery of a field prior to study design and integrate AI as a collaborative tool for ideation rather than an infallible authority (Gasparetto et al., 2025). For editors and peer reviewers, multidimensional novelty scoring (Choi & Lim, 2025) could effectively complement traditional peer review, thereby mitigating reliance on unsubstantiated "novelty claims" often presented in cover letters. For funding agencies, prioritizing interdisciplinary, team-based proposals that integrate AI-assisted literature analytics may generate outputs of heightened novelty compared to conventional investigator-initiated grants (El Arab et al., 2025). For developers of scholarly infrastructure, the development of transparent, auditable AI systems incorporating domain-specific guardrails is paramount to prevent the reinforcement of citation biases or the marginalization of non- mainstream methodologies (Mohammadi et al., 2025).

Conclusion

This review demonstrates that interdisciplinary collaboration and AI-driven methodologies represent a transformative paradigm for identifying research gaps and articulating novelty. By moving beyond conventional literature reviews toward computationally enhanced, data-driven strategies, researchers can more precisely map intellectual landscapes and target high-impact, underexplored areas. The integration of artificial intelligence into scholarly inquiry offers a powerful pathway for guiding future research with enhanced rigor, predictive capacity, and societal relevance across disciplines.

CRediT Authorship Contribution Statement

F.M.S: Conceptualization, methodology, analysis, drafting; E.P: Literature search, data extraction, validation, and critical review.

AI Assistance Declaration

The author hereby declares that, during the preparation of this manuscript, generative AI tools such as ChatGPT, Microsoft Copilot, and Google Gemini were utilized to assist with language enhancement and grammar correction. Following the use of these tools, the author thoroughly reviewed and revised the content and takes full responsibility for the final version of the manuscript, ensuring its accuracy and adherence to the required academic standards.

Conflict of Interest

The authors declare that they have no conflicts of interest.

Acknowledgement

The authors thank colleagues and reviewers for their insights during the development of this review.

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