Digital Culture & AudioVisual Challenges

Position Paper: Creating and Managing Funded Cultural Projects through Artificial Intelligence and Big Data

Eleni Christodoulopoulou, Institute for Games Research (IGR), Ionian University, Corfu, Greece, xristodelen@gmail.com

Ioannis Deliyannis, Professor, Institute for Games Research (IGR), Ionian University, Corfu, Greece,  Ionian University, Corfu, Greece, yiannis@ionio.gr

Ioannis Karydis, Associate Professor, Dept. of Informatics, Ionian University, Greece, karydis@ionio.gr

Matthew Damigos, Assistant Professor, Department of Archives, Library Science and Museology, Ionian University, Corfu, Greece, mgdamigos@ionio.gr

Keywords: Artificial Intelligence, Culture, Complex Management Processes, Complex Management: Monitoring Procedures for Cultural Projects, Big Data

Abstract
Securing funding for cultural projects can be a complex and time-consuming process, often requiring meticulous planning, persuasive proposal writing, and ongoing project monitoring. This position paper addresses those issues by proposing the development of an innovative digital framework designed to utilise the near-human reasoning abilities of artificial intelligence (AI), the information-holding capacity of Big Data and their analytic capabilities, combined with benchmarking techniques to streamline and improve the entire project lifecycle—from drafting proposals to evaluating project outcomes. This work presents the issues that arise in this particular field, one of which is the lack of a unified comprehensive system that optimises proposal development, submission, monitoring, and evaluation. By integrating AI-powered tools—such as natural language processing (NLP) for refining grant proposals and machine learning for predictive success modelling—this framework aims to enhance transparency, reduce administrative burdens, and increase the likelihood of securing funding. Our approach addresses the problem in a bottom-up manner by introducing new tools, benchmarking them, and rendering them available to our research group in order to be evaluated in real-life scenarios. Other desirable features that are addressed within this research include dynamic budget management and automated quality control, ensuring that proposals are aligned with funding criteria and cultural priorities

Research Objectives

This study will focus on several interconnected objectives designed to transform how cultural grant proposals are created and managed. The primary objective is to develop a digital framework that leverages advanced technologies to improve every stage of cultural project funding. This framework will address the complexities of securing and managing grants by integrating artificial intelligence (AI), Big Data analytics, and benchmarking techniques. Key innovations include the utilisation of natural language processing to help craft and refine grant proposals [5, 15], making them more persuasive and closely aligned with funding bodies’ expectations and criteria; the application of machine learning to analyse past funding decisions [1, 2, 7] and predict the likelihood of proposal approval; and the implementation of AI-based checks to automatically validate proposals against technical requirements and guidelines. In addition, tools that dynamically adjust and optimise project budgets within proposals may be required, so we will investigate the capabilities of AI towards helping the alignment of budget plans with evolving financial constraints and strategic priorities in the cultural sector. Finally, the framework extendability beyond the application phase [6, 14] will be examined by tracking funded projects through execution. Intelligent monitoring tools will follow project progress, budget adherence, and deliverables, providing real-time feedback and ensuring greater transparency and accountability. The implications of those objectives are clearly complex and of high importance, as we believe that the same methodology can be applied to different fields [11, 12, 13] that involve the completion of complex processes.

Methodology
This research adopts a mixed-methods approach, combining qualitative and quantitative strategies across three key phases. To achieve these objectives, the research will follow a multi-phase approach combining theory, tool development, and practical testing. We begin by studying existing frameworks and best practices in cultural funding management [5, 15] by reviewing academic research and case studies on grant writing, project management in the cultural sector, and current applications of AI in these domains. The literature review will identify gaps in the current funding process and inform the design of the new AI-driven framework [3, 4]. The next stage involves building and training the core components of the digital framework [1, 3, 7], allowing the creation of the following components: A NLP Proposal Assistant trained on a large dataset of successful cultural grant proposals to assist users in structuring their proposals, suggesting improvements in language and content to meet evaluators’ expectations, including cultural diversity and contextual considerations implementing a Machine Learning Success Predictor [1, 2], which is a predictive model using machine learning techniques to find patterns in past grant approvals that will be used to estimate the approval probability and highlight factors influencing success, guiding applicants to strengthen their submissions. The process of integrated compliance checks [2, 4] will allow for automated quality control mechanisms that review each proposal draft for completeness and compliance, where the system will flag missing information, formatting issues, or deviations from the funding call’s guidelines, allowing applicants to correct them before submission. The Dynamic Budgeting Module [6, 7] can be described as a budgeting tool that uses real-time data and constraints to help formulate realistic project budgets, and it should be able to suggest reallocations or highlight budget items that may need justification, ensuring the budget aligns with funder priorities and limits. Finally, the Project Monitoring Interface [7, 14] can be described as an interactive monitoring system to be used once projects are funded to automatically track milestones, expenses, and deliverables against the plan, alerting stakeholders to any deviations and aggregating data for reporting outcomes.

 
From that point onwards, the benchmarking and model refinement will allow result validation and improvement, while real-world testing and feedback will provide a platform to test the integrated framework in a practical setting within the research group or partner cultural institutions. In this pilot phase, users (e.g., grant writers at cultural organisations) will use the AI tools to draft and submit actual funding applications. Throughout this process, data will be collected on user experience, proposal quality, and success rates. Feedback from participants will highlight what works well and what needs adjustment. This iterative testing phase ensures the framework is user-friendly and adaptable to different types of funding programmes. Lessons learnt will be incorporated to optimise the system before wider deployment.

Conclusion
The anticipated outcomes of this research promise to reshape the landscape of cultural funding management through a robust, AI-enhanced framework [1, 3, 7, 9]. By integrating advanced natural language processing [5, 15], machine learning for predictive success modelling [2, 4], and dynamic budget management tools [6, 10], the framework is expected to streamline the proposal drafting, submission, and evaluation processes significantly.  As a result, cultural organisations will likely experience a notable reduction in administrative burdens and an increase in the overall quality and persuasiveness of their funding proposals. The adoption of automated quality control and real-time project monitoring [6, 15] will further enhance transparency and accountability throughout the project lifecycle, ensuring that proposals remain compliant with funding criteria while adapting to evolving financial constraints. Ultimately, this work aims to provide a scalable and sustainable model that not only elevates the success rates [10, 11, 13] of funding applications but also contributes to more efficient and data-driven decision-making within the cultural sector. The framework's ability to benchmark new proposals against historical successes [6, 14] is expected to foster continuous improvement, thereby empowering cultural institutions to secure funding more effectively and deliver projects that better align with strategic cultural priorities.

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