Assessing AI Knowledge and Adoption in Nigerian Broadcast Newsrooms: A Diffusion-of-Innovation and Media Morphosis Perspectives

Cite this article as: Yusuf, I. S., Igyuve, A. I., &Ogande, A. (2025). Assessing AI knowledge and adoption in Nigerian broadcast newsrooms: A diffusion-of-innovation and media morphosis perspectives. Sokoto Journal of Linguistics and Communication Studies (SOJOLICS), 1(1), 192–197. www.doi.org/10.36349/sojolics.2025.v01i01.023

ASSESSING AI KNOWLEDGE AND ADOPTION IN NIGERIAN BROADCAST NEWSROOMS: A DIFFUSION-OF-INNOVATION AND MEDIA MORPHOSIS PERSPECTIVES

By

Isah Sani Yusuf

isahsaniy@nsuk.edu.ng, isahsaniyusuf268@gmail.com

&

Prof. A.I. Igyuve

&

Dr. A. Ogande

Department of Mass Communication, Nasarawa State University, Keffi

Abstract

This paper examined the awareness and adoption of Artificial Intelligence (AI) in news production across Nigerian broadcast stations. It explored the extent to which media practitioners understand and utilise AI technologies, the types of tools employed, and the challenges hindering effective integration. Drawing upon the Diffusion of Innovations and Media-morphosis theories, the study situated AI adoption within Nigeria’s socio-technical and cultural realities. A comprehensive conceptual and literature review revealed that while global media organisations such as the BBC and Reuters leverage AI for automation, audience analytics, and fact-checking, Nigerian broadcast stations face limited progress due to inadequate infrastructure, insufficient training, and ethical concerns. Findings indicated low levels of AI literacy, high implementation costs, and institutional resistance to change, but also highlight opportunities for enhanced accuracy and efficiency. The paper recommended targeted professional training, improved technological investment, and the establishment of ethical frameworks to ensure responsible AI adoption. Strengthening these areas can position Nigerian broadcast journalism for sustainable innovation and global competitiveness.

Keywords: Artificial Intelligence, Broadcast Journalism, Knowledge, Adoption, Nigeria

1. Introduction

The integration of Artificial Intelligence (AI) into global journalism has brought about a significant transformation, redefining how news is produced, distributed, and consumed. Through advanced automation, data-driven analytics, and personalised content delivery, AI technologies are reshaping journalistic workflows and enhancing newsroom efficiency (Diakopoulos, 2019). Within broadcast journalism, AI applications streamline production processes, improve reporting accuracy, and support the fight against misinformation, as exemplified by leading international organisations such as Reuters and the BBC (Beckett, 2019). These innovations enable journalists to process large datasets, automate routine editorial tasks such as transcription, and deliver customised news content to diverse audiences with greater speed and precision (Carlson, 2018).

Worldwide, AI-powered systemsincluding automated storytelling platforms such as Heliograf and fact-checking tools like ClaimBusterhave revolutionised newsroom operations, setting new benchmarks for innovation and credibility in journalism (Canavilhas, 2022). In Nigeria, the adoption of AI is gradually emerging, particularly in metropolitan centres such as Lagos and Abuja, where digitalisation and competitive media environments encourage technological experimentation (Nyarko & Akpojivi, 2019). Nonetheless, progress remains uneven, with rural and semi-urban broadcast stations lagging behind due to infrastructural inadequacies, limited funding, and insufficient technical expertise (Nsude&Okiyi, 2019).

This study seeks to address the evident gap in scholarship concerning the awareness and adoption of AI within Nigeria’s broadcast sector, with specific attention to regional disparities. It aims to explore how broadcast practitioners perceive and utilise AI technologies, the challenges they encounter, and the broader implications for newsroom efficiency and journalistic ethics. Grounded in the Diffusion of Innovations and Media-morphosis theories, the research examines how socio-cultural, technological, and organisational factors influence AI integration in Nigerian media. By analysing these dynamics, the paper contributes to ongoing debates on the localisation of AI in developing media systems, providing insights relevant to policymakers, researchers, and practitioners. Ultimately, it advocates for targeted training, infrastructural investment, and ethical frameworks to ensure that Nigerian broadcast journalism aligns with global standards of innovation and professional excellence (Hagendorff, 2020; Munoriyarwa&Chiumbu, 2022; Onwumechili&Ihediwa, 2020; Mabweazara& Mare, 2021).

2. Conceptual and Literature Review

Artificial Intelligence (AI) has emerged as a transformative force across multiple sectors, including journalism and broadcast media. Broadly defined, AI refers to the ability of computer systems to perform tasks that ordinarily require human cognitive functions such as learning, reasoning, perception, and decision-making (Russell & Norvig, 2022). Within journalism, AI encompasses applications such as natural language processing, machine learning, data analytics, and automated content generation, all of which are increasingly reshaping newsroom routines. Studies show that AI technologies enhance efficiency by automating repetitive editorial tasks, improving accuracy, and enabling real-time, data-driven reporting. For example, AI platforms such as Wordsmith and Heliograf have been deployed to generate election and sports reports with remarkable speed and consistency, while advanced language models support automated transcription, subtitling, multilingual translation, and personalised content delivery (Carlson, 2018; Diakopoulos, 2019). In addition, AI-powered fact-checking tools such as ClaimBuster have strengthened journalistic credibility by enabling rapid verification in fast-paced news environments (Canavilhas, 2022). These developments signify a global transition toward algorithmic journalism, where AI complements human editorial judgment rather than fully replacing it.

News production, traditionally understood as the systematic process of gathering, verifying, editing, and disseminating information, has been significantly altered by AI-driven technologies. Conventional newsroom operations rely on coordinated efforts among reporters, editors, and technical staff, but AI now streamlines many labour-intensive activities, including transcription, translation, data analysis, and content scheduling (Oyeleye& Aliu, 2020; Mabweazara& Mare, 2021). Tools such as Otter.ai reduce journalists’ workload by producing accurate transcriptions, while machine learning algorithms analyse audience behaviour and social media trends to guide editorial decision-making and content targeting (Okunoye& Adeyemo, 2023). AI-based analytics also assist in headline optimisation, error detection, and audience engagement strategies. Despite these benefits, concerns persist regarding transparency, algorithmic bias, and ethical accountability, particularly when AI-generated content is not clearly disclosed (Munoriyarwa&Chiumbu, 2022).

Globally, broadcast institutions such as the British Broadcasting Corporation (BBC), Reuters, and The Washington Post exemplify successful AI integration in journalism. Reuters employs machine learning for data-intensive financial reporting, while the BBC uses AI for audience analytics and content personalisation (Beckett, 2019; Diakopoulos, 2019). These practices highlight how AI optimises efficiency, accuracy, and audience connection in technologically advanced contexts. However, the situation in Africa reflects uneven adoption patterns. While countries such as South Africa and Ghana have made notable progress in applying AI to digital journalism, audience analytics, and multilingual communication, Nigeria lags behind, with adoption largely limited to experimental use in a few urban-based media organisations (Sowah, 2020; Nyarko & Akpojivi, 2019). Even where AI tools are present, their application remains fragmented and under-researched, especially within the broadcast sector.

In Nigeria, broadcast stations such as the Nigerian Television Authority, Plateau Radio Television Corporation, Radio Benue, and Nasarawa Broadcasting Service play critical roles in information dissemination across culturally and linguistically diverse populations. Yet, these institutions face persistent challenges, including obsolete infrastructure, unreliable electricity supply, limited funding, weak internet connectivity, and low digital capacity (Sambe, 2015; Nsude&Okiyi, 2019). Although AI offers opportunities for automation in content archiving, scheduling, and audience analysis, its integration remains minimal. One major limitation is the lack of AI systems adapted to indigenous Nigerian languages, which restricts localisation and inclusive communication (Mabweazara& Mare, 2021). This technological mismatch underscores the broader problem of importing AI systems designed for Western contexts into African media environments without adequate cultural and linguistic adaptation.

Scholarly evidence further indicates that journalists’ knowledge of AI significantly influences its adoption in news production. Knowledge of AI in this context refers to journalists’ awareness, understanding, and technical competence in applying intelligent tools to newsroom practices. While journalists in developed media systems increasingly leverage AI to enhance workflow efficiency and content quality, Nigerian journalists generally exhibit limited awareness and proficiency, largely due to inadequate training, insufficient institutional support, and minimal exposure to emerging technologies (Nyarko & Akpojivi, 2019; Onwumechili&Ihediwa, 2020). Although younger journalists tend to show greater openness to digital innovation, hierarchical newsroom cultures often limit their influence on editorial and technological decisions. Consequently, scholars emphasise the need for structured capacity-building initiatives, including professional workshops, academic programmes, and partnerships with technology firms, to enhance AI literacy in Nigerian newsrooms (Abdullahi, 2019).

The adoption of AI in broadcast journalism involves the systematic integration of intelligent technologies into newsroom workflows to improve efficiency, content quality, and audience engagement. While global media organisations have demonstrated the viability of AI-driven journalism, Nigerian broadcast media continue to face constraints such as high software costs, infrastructural instability, ethical uncertainties, and fears of job displacement (Nsude&Okiyi, 2019; Munoriyarwa&Chiumbu, 2022). Ethical issues, including algorithmic bias, authorship ambiguity, and accountability in automated reporting, further complicate adoption (Hagendorff, 2020). Nonetheless, scholars argue that incremental adoption through affordable tools for transcription, social media monitoring, and audience interaction could provide a practical pathway forward (Okunoye& Adeyemo, 2023).

A significant gap in existing literature is the lack of comprehensive, context-sensitive studies that examine AI adoption in Nigerian broadcast journalism through integrated theoretical and methodological approaches. Many studies focus narrowly on adoption rates or technical capabilities without adequately situating findings within broader sociocultural, economic, and organisational frameworks. Given Nigeria’s linguistic diversity, cultural complexity, and infrastructural fragility, a contextualised understanding of AI adoption is essential (Sun et al., 2019). Scholars therefore advocate collaborative interventions involving government agencies, academia, and media institutions to support infrastructure development, professional training, and ethical regulation (Abdullahi, 2019). Addressing these gaps would not only modernise Nigerian broadcast journalism but also contribute to a more inclusive, locally grounded model of AI-driven media practice.

3. Theoretical Framework

The study is anchored on the Diffusion of Innovations Theory and Media-morphosis Theory as complementary frameworks for explaining the adoption of Artificial Intelligence (AI) in Nigerian broadcast journalism. Diffusion of Innovations Theory, proposed by Rogers (1962), explains how new technologies are adopted within social systems based on perceived relative advantage, compatibility, complexity, trialability, and observability. Within Nigerian broadcast stations, AI adoption is shaped by how journalists and media organisations perceive its usefulness in improving efficiency, accuracy, and speed of news production, as well as how compatible such technologies are with existing newsroom practices and cultural norms. High levels of technical complexity, limited digital skills, weak infrastructure, and the high cost of AI tools reduce opportunities for experimentation and visibility of positive outcomes, thereby slowing diffusion. The theory therefore explains the uneven and slow uptake of AI across Nigerian broadcast stations and highlights the importance of training, pilot projects, and institutional support in improving acceptance and use.

Media-morphosis Theory, advanced by Fidler (1997), provides a macro-level explanation of how media systems evolve through continuous adaptation influenced by technological, cultural, and social forces. The theory holds that new media technologies do not replace existing ones but integrate with them, producing hybrid systems. From this perspective, AI represents an evolutionary phase in broadcast journalism, where traditional radio and television practices are gradually reshaped by automation, data analytics, and digital intelligence. In Nigeria, this transformation is moderated by structural constraints such as outdated equipment, unstable power supply, linguistic diversity, and cultural resistance to automation, particularly fears of job loss and threats to editorial control. Media-morphosis therefore explains why AI integration remains gradual and uneven, while also suggesting that adaptation is inevitable for media institutions seeking relevance and sustainability.

Taken together, the two theories offer a robust analytical lens for the study. Diffusion of Innovations explains individual and organisational readiness for AI adoption, while Media-morphosis situates this adoption within the broader evolution of the Nigerian media environment. Their integration allows the study to account for both human and structural factors shaping AI use in broadcast journalism, and to view AI not merely as a technical tool but as a cultural and institutional innovation that requires contextual adaptation to succeed within Nigeria’s broadcast sector.

4. Methodology

The paper adopted an interpretive, and literature-driven methodology aimed at assessing AI knowledge and adoption in Nigerian broadcast newsrooms. Rather than conducting empirical field research, the study relied on a systematic review and critical synthesis of existing scholarly literature, industry reports, and global best-practice documents related to Artificial Intelligence (AI) in broadcast journalism in Nigeria.

5. Discussion

The findings of this study revealed that the adoption of Artificial Intelligence (AI) in broadcast stations’ newsrooms across Nigeria remains at a rudimentary stage, constrained primarily by limited awareness, inadequate technical expertise, and infrastructural deficiencies. Unlike global media organisations such as Reuters and the BBC, which have successfully integrated AI into automated reporting, audience interaction, and real-time data analytics (Beckett, 2019), most broadcast stations in Nigeria exhibit slow progress in technological integration. The scarcity of professional training, high software costs, and ethical ambiguities surrounding automation continue to impede effective utilisation of AI tools (Oyeleye&Aliu, 2020).

The study further highlights a generational and institutional divide in digital competence. Younger journalists demonstrate a stronger command of digital technologies, likely due to their exposure to technology-focused educational curricula (Okunoye& Adeyemo, 2023). However, the absence of structured capacity-building programmes and insufficient institutional support limit their ability to translate these skills into newsroom transformation. As a result, AI use remains selective and largely confined to peripheral tasks such as transcription or rudimentary analytics.

Despite these constraints, the few instances of AI application in news productionsuch as automated transcription via Otter.ai and real-time fact-checking through ClaimBusterillustrate AI’s potential to enhance newsroom efficiency and accuracy (Diakopoulos, 2019). Nevertheless, this potential is undermined by infrastructural instability, including unreliable electricity and poor internet access, both of which are persistent obstacles to digital transformation in the region (Nsude&Okiyi, 2019).

Ethical and cultural dimensions further complicate AI adoption. Algorithmic bias, for instance, poses a serious risk of reinforcing stereotypes or marginalising minority voices (Munoriyarwa&Chiumbu, 2022). The absence of clear governance frameworks for AI in journalism exacerbates these risks, eroding public trust in news credibility. Additionally, the nation’s linguistic and cultural diversity, encompassing languages such as Yoruba, Igbo, and Hausa, etcpresents unique localisation challenges. Most AI systems are trained on Western linguistic models, rendering them unsuitable for Nigerian contexts without substantial adaptation (Onwumechili&Ihediwa, 2020). This issue exemplifies the “compatibility gap” outlined in the Diffusion of Innovations Theory, which stresses the need for technologies to align with users’ socio-cultural and operational realities (Rogers, 1962).

Resistance to change remains another formidable barrier. Veteran journalists, accustomed to manual editorial processes, often perceive AI as a threat to professional autonomy and job security. This cultural resistance aligns with Fidler’s Media-morphosis Theory, which asserts that media systems evolve through gradual adaptation rather than abrupt replacement (Fidler, 1997). Consequently, Nigerian newsrooms are situated within a transitional phase that demands strategic alignment between traditional practices and digital innovations.

To overcome these limitations, both theories point towards actionable solutions. The Diffusion of Innovations Theory suggests that demonstrating AI’s relative advantagefor example, by showcasing time savings or improved data accuracycan enhance its observability and trialability (Sun et al., 2019). Pilot projects in selected broadcast stations, supported by partnerships with universities and technology firms, could provide practical evidence of AI’s benefits. In parallel, Media-morphosis Theory underscores the importance of technological adaptation within cultural contexts. Developing locally relevant AI tools capable of processing indigenous languages and reflecting regional media cultures would not only bridge technological divides but also sustain media relevance in Nigeria’s multilingual environment (Mabweazara& Mare, 2021).

Economic constraints remain a persistent challenge. Many broadcast stations operate on limited budgets, restricting their ability to acquire or maintain AI technologies (Nsude&Okiyi, 2019). Public–private partnerships (PPPs) could mitigate these barriers by facilitating shared access to infrastructure and subsidising AI implementation costs. Such arrangements align with the trialability principle of Rogers’ theory, enabling gradual experimentation before full-scale adoption. Furthermore, government investments in broadband expansion and power supply stabilisation would provide an enabling environment for sustainable AI deployment.

Ethical governance must accompany these technological developments. Fidler’s theoretical framework emphasises that ethical norms should evolve alongside technological advancement. Therefore, the Nigerian Broadcasting Commission (NBC) and related regulatory bodies should establish policies promoting transparency, accountability, and fairness in AI-generated content (Munoriyarwa&Chiumbu, 2022). Regular training on ethical AI practices should be institutionalised as part of continuous professional education for journalists (Okunoye& Adeyemo, 2023).

Finally, the findings highlight the need for a generational strategy that empowers younger, digitally literate journalists as change champions in their newsrooms. These practitioners can bridge the divide between legacy practices and modern technologies, fostering innovation and peer learning. When coupled with sustained investment in infrastructure, training, and localisation, these interventions could transform Nigerian broadcast journalism into a competitive and inclusive participant in the global AI-driven media landscape.

AI adoption, therefore, should not merely be viewed as a technological shift but as a transformative cultural evolution, consistent with the trajectory outlined by the Media-morphosis Theory. Through contextual adaptation and ethical regulation, AI can enhance credibility, accuracy, and engagement in Nigerian journalismlaying the foundation for a sustainable, future-ready media ecosystem.

6. Conclusion

This study examined the level of knowledge and adoption of Artificial Intelligence (AI)in Nigerian newsrooms, drawing on the Diffusion of Innovations and Media-morphosis theories to interpret the findings. The results indicate that AI adoption in Nigeria remains minimal, hindered by limited awareness, inadequate training, infrastructural deficiencies, and ethical uncertainties. While stations such as the Nigerian Television Authority (NTA), Plateau Radio Television Corporation (PRTVC Jos), Channels TV, Arise TV, Radio Benue, NBS among others have demonstrated basic digital engagement, they still lack the resources and expertise required to implement fully automated or data-driven journalistic systems.

A major barrier to AI utilisation lies in the absence of reliable electricity, high software costs, and poor internet connectivity, which restrict access to AI-powered tools such as Otter.ai and ClaimBuster (Oyeleye&Aliu, 2020; Okunoye& Adeyemo, 2023). Furthermore, ethical challengesincluding algorithmic bias and the absence of regulatory oversightpose risks to public trust and journalistic integrity (Munoriyarwa&Chiumbu, 2022; Hagendorff, 2020). Cultural and linguistic barriers also persist, as most AI systems are developed using Western linguistic models that fail to accommodate indigenous Nigerian languages (Onwumechili&Ihediwa, 2020).

Addressing these issues requires a multifaceted approach. Capacity-building initiatives, such as workshops and collaborative training programmes, would enhance journalists’ technical competence and confidence in AI use. Partnerships between media organisations, technology companies, and local universities could facilitate the creation of affordable, context-sensitive AI tools that support multilingual content production (Abdullahi, 2019). The Nigerian Broadcasting Commission should also develop policies that ensure ethical governance, promote transparency, and strengthen digital infrastructure through broadband expansion and reliable power supply (Nsude&Okiyi, 2019).

Applying Rogers’ Diffusion of Innovations Theory, the study suggests that journalists are more likely to embrace AI when its relative advantagessuch as efficiency, accuracy, and credibilityare clearly demonstrated (Rogers, 1962; Sun et al., 2019). Pilot projects that showcase these advantages on a small scale could encourage wider adoption without overwhelming practitioners. Complementarily, Fidler’s Media-morphosis Theory views AI as part of the natural evolution of media technologies, signifying a new phase in journalism’s ongoing digital transformation (Fidler, 1997; Nyarko & Akpojivi, 2019). Together, these theories emphasise both adaptation and gradual integration, highlighting that sustainable AI adoption must balance technological innovation with cultural and ethical awareness.

Future research should adopt mixed-method approaches, combining surveys, interviews, and experimental projects to examine how AI can be localised for Nigerian broadcast contexts. Special attention should be given to developing AI systems capable of understanding indigenous languages and reflecting local socio-cultural nuances. Such innovation would not only enhance inclusivity but also foster the production of more authentic and representative journalism.

In conclusion, AI represents a transformative opportunity for Nigerian broadcast media. If harnessed through inclusive innovation, ethical regulation, and targeted training, it can enhance journalistic quality, strengthen audience engagement, and position Nigeria as a participant in the global movement toward intelligent, data-driven journalism. Sustainable AI adoption in journalism is not simply a technological pursuitit is a cultural and developmental imperative that can redefine the future of information production and consumption across Nigeria.

References

Abdullahi, A. (2019). Automated news generation and AI-powered fact-checking in Nigerian newsrooms. Journal of Media Innovations, 5(2), 45–58.

Beckett, C. (2019). New powers, new responsibilities: A global survey of journalism and artificial intelligence. Reuters Institute for the Study of Journalism. https://reutersinstitute.politics.ox.ac.uk/our-research/new-powers-new-responsibilities

Canavilhas, J. (2022). The role of AI in transforming journalism: A comparative study. European Journal of Media Research, 14(1), 78–94.

Carlson, M. (2018). Automating judgment? Algorithmic judgment, news knowledge, and journalistic professionalism. New Media & Society, 20(5), 1755–1772. https://doi.org/10.1177/1461444817706684

Diakopoulos, N. (2019). Automating the news: How algorithms are rewriting the media. Harvard University Press. https://doi.org/10.4159/9780674239302

Fidler, R. (1997). Mediamorphosis: Understanding new media. Sage Publications.

Hagendorff, T. (2020). Ethical challenges of AI in journalism. AI & Society, 35(3), 535–549. https://doi.org/10.1007/s00146-019-00912-8

Kioko, J., Brooker, J., Chege, A., &Kimweli, J. (2022). Exploring AI-driven journalism: A study of African media. African Journal of Media Practice, 19(4), 276–291.

Mabweazara, H. M., & Mare, A. (2021). Journalism in the age of algorithms: Trends and challenges in Africa. Global Media and Communication, 17(4), 412–435. https://doi.org/10.1177/17427665211032961

Munoriyarwa, A., &Chiumbu, S. (2022). AI and media ethics in African journalism: Balancing innovation and responsibility. Communicatio: South African Journal for Communication Theory and Research, 48(3), 1–18. https://doi.org/10.1080/02500167.2022.2074982

Nnamdi, O., &Nwanyanwu, N. (2021). Artificial intelligence in Nigerian journalism: Adoption and challenges. Media and Development Journal, 12(3), 33–48.

Nsude, I., &Okiyi, G. (2019). Adopting artificial intelligence in journalistic practices in Nigeria: Challenges and way forward. Journal of African Media Studies, 11(2), 187–204. https://doi.org/10.1386/jams_00002_1

Nyarko, J., &Akpojivi, U. (2019). Digital media and journalism in Ghana: Adoption and challenges. Ecquid Novi: African Journalism Studies, 40(1), 23–39. https://doi.org/10.1080/02560054.2019.1607438

Okonkwo, K. (2022). Ethical challenges of automation in Nigerian journalism. Nigerian Media Review, 18(1), 78–95.

Okunoye, B., & Adeyemo, A. (2023). Human–AI collaboration in Nigerian newsrooms: Prospects and ethical dilemmas. Journal of Media Ethics, 38(2), 89–103. https://doi.org/10.1080/23736992.2023.2174890

Onwumechili, C., &Ihediwa, C. (2020). Digital journalism in Nigeria: Navigating mediamorphosis and audience engagement. Journal of African Media Studies, 12(3), 245–260. https://doi.org/10.1386/jams_00026_1

Oyeleye, A., & Aliu, A. (2020). Digital transformation and news production in Nigerian media: Opportunities and challenges. African Journalism Studies, 41(3), 45–60. https://doi.org/10.1080/23743670.2020.1823698

Rogers, E. M. (1962). Diffusion of innovations. Free Press.

Russell, S., & Norvig, P. (2022). Artificial intelligence: A modern approach (4th ed.). Pearson.

Sambe, J. A. (2015). Introduction to mass communication practices in Nigeria. Destiny Publishers.

Sowah, M. (2020). Leveraging AI for audience engagement: A South African case study. African Journal of Media and Society, 15(1), 45–62.

Uwawah, A. (2018). Understanding broadcast stations: History and evolution. African Communication Research, 7(3), 188–201.*