1. Introduction
Artificial intelligence (AI) has significantly transformed the global journalism landscape, offering tools that range from automated news generation to real-time verification and fact-checking (Shweki et al., 2025). While major media organizations worldwide have quickly embraced these innovations, their adoption in local media, particularly Palestinian radio stations, remains limited and underexplored (Element Media, 2023). Radio stations in Hebron face unique challenges, including constrained resources, political restrictions, and evolving audience expectations, yet there is growing interest among media professionals in leveraging AI technologies such as natural language processing, machine learning algorithms, and automated verification systems to enhance news quality and credibility (Shweki et al., 2025; Element Media, 2023). Local initiatives, such as the Tahqeeq tool developed by Hebron University students, exemplify emerging engagement with AI in journalism (Hebron University, 2023).
Artificial intelligence is increasingly influencing the generation and verification of news worldwide. Despite this trend, the extent to which Palestinian radio stations, including those in Hebron, have adopted and used AI tools remains largely unknown. Studying how these technologies are applied in local radio stations can provide important insights into their impact on the accuracy, efficiency, and quality of news generation and verification.
This study examines how Palestinian radio staff in Hebron adopt AI technologies for news production and verification, aiming to understand the extent of their use, the perceived benefits, and the challenges encountered in practice. The main research question guiding the study is how AI tools are adopted and utilized by radio staff, and what impact these tools have on news quality, accuracy, and workflow efficiency.
1.1. Research objectives
General Objective:
The primary objective of this study is to examine the adoption of artificial intelligence technologies by Palestinian radio workers in Hebron and to evaluate their impact on news generation and verification processes.
Specific Objectives:
- OB1: Identify the types of AI technologies used by radio staff for producing and verifying news.
- OB2: Analyze how AI tools are incorporated into the daily workflow of newsrooms.
- OB3: Evaluate the perceived benefits of using AI in improving news quality, accuracy, and efficiency.
- OB4: Investigate the challenges and obstacles faced by staff when implementing AI technologies.
1.2. Artificial intelligence in radio newsrooms
Since the launch of ChatGPT in 2022, discussions regarding the transformative role of artificial intelligence (AI) in professional work have intensified, particularly in radio broadcasting (Fadilah, et al, 2025). While human expertise remains central to journalistic practice, several radio stations have begun integrating AI technologies to support newsroom operations, highlighting the growing relevance of automated tools in drafting scripts, verifying language, and adapting traditional reporting for digital platforms (Tejedor Calvo et al., 2024). However, only a few systems, such as ChatGPT, BLOOM, and ArticleForge, provide sufficiently comprehensive functionalities to be considered meaningful investments for media organizations aiming to improve both efficiency and content quality.
In radio production, AI is applied across a wide spectrum of processes, from automating routine tasks such as song selection and scheduling to generating news content, thereby enhancing operational efficiency and improving the overall listener experience (Furtáková, 2023). Furthermore, AI democratizes technical capabilities, making advanced tools like noise reduction, voice enhancement, and generative content creation available even to smaller stations, including municipal and school radios (Ribes Guardia, Monclús Blanco & Terol-Bolinches, 2025). By levelling the technical playing field, these tools allow smaller broadcasters to compete in terms of production quality while fostering innovation and creativity, enabling programs to engage audiences more effectively in a media environment increasingly dominated by podcasts and digital streaming (Harliantara, 2024).
Despite these opportunities, skepticism and caution remain among media professionals. Concerns over job security, implementation costs, limited training, and ethical implications underscore the need for a careful balance between technological integration and human judgment (Munoriyarwa, Chiumbu & Motsaathebe, 2021). While AI can streamline repetitive tasks and increase productivity, it remains prone to errors, lacks deep contextual understanding, and may pose ethical or privacy risks. Professional oversight is therefore essential to ensure accuracy, maintain editorial coherence, and uphold journalistic standards (Furtáková, 2023; Tejedor Calvo et al., 2024; Ribes Guardia et al., 2025).
AI is reshaping newsrooms, presenting both opportunities and challenges simultaneously. On the positive side, it frees journalists from low-value, repetitive tasks, allowing them to focus on qualitative reporting, investigative work, and the creation of high-quality content, potentially revitalizing journalism beyond the routine transcription of facts (Noain-Sánchez, 2022). AI also enables content personalization and the generation of news on a wider array of topics, helping media organizations reach broader audiences. However, this autonomy introduces risks such as filter bubbles, reduced diversity, and potential editorial bias, emphasizing the continued necessity of critical human evaluation and supervision (Noain-Sánchez, 2022).
Building on these considerations, the integration of AI in newsrooms highlights the importance of ethical considerations, clear delineation of responsibilities, and continuous professional training. Collaboration between journalists, AI developers, and programmers is crucial to ensure that AI supports rather than replaces human judgment. Ethical guidelines, accountability mechanisms, and transparency standards must be embedded in all stages of AI design and implementation, from content creation to distribution, to safeguard journalistic integrity and prevent misuse, such as the dissemination of misleading information or unauthorized voice cloning (Abdelfattah Ramadan, 2021; Noain-Sánchez, 2022; Tejedor Calvo et al., 2024; Ribes Guardia et al., 2025).
Looking ahead, AI presents both transformative potential and challenges for the future of radio newsrooms. While it can enhance efficiency, foster creativity, and improve audience engagement, increasing the autonomy of AI tools necessitates careful monitoring to prevent bias, maintain diversity, and ensure accountability (Noain-Sánchez, 2022). Continuous education, interdisciplinary collaboration, and the development of comprehensive ethical codes are essential for maximizing the benefits of AI while minimizing risks. AI should serve as a supportive tool that complements journalists' expertise, judgment, and ethical responsibility, preserving the depth, nuance, and credibility intrinsic to quality journalism.
1.3. Artificial intelligence tools and techniques for news verification
The rapid circulation of false or misleading information poses a significant challenge to public perception, social stability, and trust in the media (Khodary, 2025; Fieiras Ceide, Vaz Álvarez & Túñez López, 2022). With the expansion of digital platforms and social media, misinformation spreads instantly, often without verification, highlighting the importance of artificial intelligence (AI) in modern newsrooms (Al-Khatem, Al-Haj Al-Tayeb & Ibrahim, 2024). AI facilitates automated fact-checking, source verification, and sophisticated data analysis, enabling journalists to deliver accurate, dependable, and ethically responsible reporting (Uthman, 2024).
Managing the growing volume and diversity of information remains challenging, yet AI applications, including machine learning algorithms, help detect inconsistencies, track information flows, and evaluate the credibility of multiple sources (Fieiras Ceide, Vaz Álvarez & Túñez López, 2022; Khodary, 2025). By automating routine verification tasks, journalists can focus on editorial judgment, contextual understanding, and critical evaluation, thereby improving the depth and reliability of reporting and reinforcing public trust in the media.
Fake news and deliberate misinformation continue to threaten journalistic integrity globally. Models such as Support Vector Machines (SVM) have proven highly effective, achieving up to 98.60% accuracy by analyzing only the first thousand characters of news articles (Abu Nasser & Abu-Naser, 2024; Madhan, Sreeja, Gokul, Haribabu & Kumar, 2024). Similarly, Natural Language Processing (NLP) techniques identify subtle linguistic patterns indicative of misleading content, and when combined with structured fact-checking and source evaluation, AI provides a robust framework for countering deceptive information effectively (Fieiras Ceide, Vaz Álvarez & Túñez López, 2022).
AI also enhances newsroom efficiency through automated processes such as fact extraction, evidence retrieval, and multimodal verification, allowing journalists to focus on nuanced editorial tasks that require ethical reasoning and contextual interpretation (Al-Khatem, Al-Haj Al-Tayeb, & Ibrahim, 2024; Sprinchinat, 2025). Human oversight remains essential, as journalists interpret complex information and apply ethical judgment. Proper adaptation of AI tools to specific operational environments, along with targeted professional training, ensures that technology complements human expertise without replacing it (Fieiras Ceide, Vaz Álvarez, & Túñez López, 2022: Uthman, 2024).
Consequently, the prevalence of AI-driven misinformation underscores the need for robust verification strategies, ethical standards, and comprehensive training for media professionals. Integrating AI with human judgment enables newsrooms to strike a balance between innovation, efficiency, and credibility, ensuring that journalism remains dependable, socially responsible, and capable of sustaining public trust in the digital era (Al-Khatem, Al-Haj Al-Tayeb & Ibrahim, 2024; Uthman, 2024; Khodary, 2025).
1.4. Artificial intelligence and automated news generation
Artificial Intelligence (AI) is increasingly transforming newsrooms by enabling the automated generation of large volumes of timely, accurate, and readable news content. Techniques such as natural language generation (NLG) and machine learning allow AI systems to draft reports on routine topics, summarize complex datasets, and maintain factual reliability without direct human input (La-Rosa Barrolleta & Sandoval-Martín, 2024). This capacity not only streamlines standard reporting but also frees journalists to focus on tasks requiring critical judgment, investigative analysis, and contextual interpretation.
The integration of AI in news production supports personalized and interactive experiences, while public perception of AI-generated content depends on its accuracy, impartiality, and credibility (İlknur Aydoğdu Karaaslan, Baha Ahmet Yılmaz & Yağmur Karadağ, 2024). By automating repetitive tasks, newsrooms can deliver content more rapidly, though AI cannot replace human editorial oversight. Ethical considerations, including bias, transparency, and the maintenance of editorial control, are crucial to the responsible adoption of AI (Yuan, Shi & Li, 2024).
Advanced AI tools, including ChatGPT, DeepSeek, Grok AI, Manus AI, and Gemini, demonstrate potential to increase productivity, support personalized audience engagement, and automate repetitive editorial work. Effective integration requires upskilling media professionals, establishing governance frameworks, and ensuring that AI complements human expertise rather than replacing it (Adinath & Smiju, 2025). These tools provide distinct advantages in automated linguistic generation, predictive analysis, and practical applications in newsroom workflows.
Large Language Model (LLM)-based systems extend the capabilities of automated news generation by synthesizing information from multiple sources, generating unique headlines and articles, and deploying content autonomously to news platforms. This reduces redundancy, ensures timely coverage, and enables continuous publication without human intervention (Neupane, Khanal, Nepal & Dangi, 2025). Platforms that rely on automated aggregation and summarization systems reflect this approach by collecting news from multiple sources, generating concise summaries, and personalizing content to reduce information overload. Research shows that AI-driven tools are widely used for automated content creation, data analysis, and audience personalization, enabling more efficient news distribution and tailored updates for users (Banafi, 2024).
Despite their efficiency, AI-generated news systems exhibit inherent biases. Advanced large language models, including recent GPT versions and Microsoft Copilot–based systems, vary in subjectivity and lexical choices. More sophisticated models tend to align more closely with expert assessments, underscoring the role of model advancement in improving output quality and neutrality (Castillo-Campos, Varona-Aramburu & Becerra-Alonso, 2024). These findings emphasize the importance of human guidance in ensuring accuracy, impartiality, and ethical integrity in news production (Pleios & Tastsoglou, 2025).
Overall, advancements in AI suggest that hybrid models, which combine automated efficiency with human oversight, can enhance both routine and complex journalistic tasks. AI supports content creation, aggregation, and curation, while editorial supervision ensures adherence to ethical standards, accuracy, and social responsibility (Li, 2025). Nevertheless, challenges such as incomplete data, copyright issues, opaque sources, and the potential for fake news must be managed carefully to preserve credibility in automated news workflows (La-Rosa Barrolleta & Sandoval-Martín, 2024: Pleios & Tastsoglou, 2025).
2. Methodology
This study employs a field-based, quantitative survey design to examine the adoption and use of artificial intelligence (AI) technologies by Palestinian radio staff in Hebron for news generation and verification. The survey approach enables the study to quantify adoption patterns and staff perceptions across multiple stations, providing a structured snapshot of how AI tools are being integrated into local newsroom routines (Sonni et al., 2024).
2.1. Research design
This study employs a descriptive–analytical research design with a quantitative approach. The design relies on structured survey data to identify patterns and levels of artificial intelligence (AI) adoption in radio newsrooms, while examining journalists’ perceptions, experiences, and professional evaluations of AI use in news production and verification processes through statistical analysis. This approach enables a comprehensive assessment of both the extent of AI integration and its perceived impact on newsroom practices within the Palestinian radio context.
2.2. Study setting and participants
According to the Palestinian Ministry of Telecommunications and Information Technology (2025), there are fifty-five radio stations operating in the West Bank and eleven in the Gaza Strip. According to WAFA (2025), many radio stations in Gaza were severely affected and ceased operations during the Israeli war.
The present study was conducted across fourteen radio stations in Hebron, a major governorate in the southern West Bank, representing both urban and rural areas. Based on data from the Journalists’ Syndicate in Hebron, the target population comprised approximately 90 newsroom professionals, including journalists, editors-in-chief, broadcasters, technicians, and content producers. A purposive sampling strategy was employed to reach employees working in these radio stations. A total of 75 valid questionnaires were collected, yielding a response rate of 83.3%. The remaining 15 individuals either declined participation or did not complete the questionnaire.
2.3. Data collection tools and procedures
Data were collected using a structured questionnaire designed to assess the extent of artificial intelligence (AI) adoption, types of technologies used, perceived benefits, challenges, and future outlook. The questionnaire comprised five sections:
- Background information: Gender, age, educational level, years of professional experience, and job title.
- AI adoption: Personal ability to use AI tools, the level of AI integration within the radio station, and the frequency of AI use in news text generation, verification, and analysis.
- Perceived benefits: The importance of AI in enhancing work speed, accuracy, content quality, and verification efficiency.
- Barriers and concerns: Challenges related to limited technical skills, financial constraints, content credibility, and concerns about potential job displacement.
- Future outlook: Expectations regarding the future role of AI in newsrooms and staff training needs.
The questionnaire was administered in an electronic format through face-to-face distribution, by visiting all radio station premises in Hebron Governorate, where participants were provided with a link to complete the questionnaire online. The questionnaire was hosted on Google Forms and data collection was conducted between 22 June 2025 and 13 September 2025.
2.4. Instrument development and validation
Some of the questionnaire items were derived from a research instrument previously used by the first author in a doctoral dissertation, while the remaining items were developed specifically for the current study to reflect the local context of Palestinian radio stations. Prior to distribution, the questionnaire was reviewed by three experts in media and communication to ensure content validity, clarity, and relevance to the study objectives.
Construct validity was examined using item-total correlation coefficients. All questionnaire items demonstrated statistically significant positive correlations with their respective dimensions, indicating satisfactory construct validity and confirming that the instrument adequately measured the intended concepts.
To assess reliability, Cronbach’s alpha coefficients were calculated for all study dimensions. The results indicated satisfactory internal consistency across the scales. Cronbach’s alpha values were 0.882 for AI Adoption, 0.942 for Perceived Benefits, 0.727 for Risks and Concerns, 0.826 for Barriers to Adoption, and 0.866 for Future Prospects. All values exceeded the recommended threshold of 0.70, indicating good reliability and suitability of the instrument for statistical analysis.
| Study Dimension | Cronbach's Alpha |
|---|---|
| AI Adoption | 0.882 |
| Perceived Benefits | 0.942 |
| Risks and Concerns | 0.727 |
| Barriers to Adoption | 0.826 |
| Future Prospects | 0.866 |
Note: All Cronbach’s alpha values exceeded the recommended threshold of 0.70, indicating satisfactory internal consistency.
2.5. Ethical considerations
Participation in this study was voluntary, and all participants were informed of the research's purpose before completing the questionnaire. Respondents were assured that their participation would be anonymous and that the collected data would be treated confidentially and used solely for academic research. No personally identifiable information was collected, and all findings are reported in aggregate form to protect participants’ privacy.
2.6. Generative AI Statement
During the preparation of this manuscript, generative artificial intelligence tools were used on a limited basis to assist with language editing, translation support, reference formatting, and proofreading. These tools were not used to generate research data, perform statistical analyses, interpret the results, or develop the study’s conclusions. All research activities, data analysis, interpretation of findings, and final decisions regarding the content of the manuscript were carried out by the authors, who assume full responsibility for its accuracy and integrity.
3. Results
Data were analyzed using the Statistical Package for the Social Sciences (SPSS). Descriptive statistics, including frequencies, percentages, means, and standard deviations, were used to summarize respondent characteristics and patterns of AI adoption. In addition, inferential statistical techniques were employed to examine differences between groups and relationships among study variables, including independent-samples t-tests, one-way analysis of variance (ANOVA), and correlation analysis.
3.1. Demographic Profile of Respondents
Figure 1. Gender distribution of respondents
Source: Authors
The sample shows that two-thirds of participants were male (65.3 %), while just over one-third were female (34.7 %). This gender imbalance suggests that men continue to hold a larger share of positions in local Palestinian radio stations, particularly in the southern West Bank, reflecting the broader trend of male dominance in technical and editorial roles in the media.
Figure 2. Age categories of respondents
Source: Authors
The age distribution shows that the majority of respondents were between 25 and 44 years old (72 %), with the 35–44 age group being the most common. Only a small fraction was under twenty-five or above 45. This suggests that local radio institutions rely primarily on mid-career professionals who combine youthful adaptability with significant practical experience, while fewer very young entrants or senior professionals are currently active in these organizations.
Figure 3. Educational background of radio staff
Source: Authors
More than half of the participants held a bachelor’s degree (54.7 %), followed by diploma holders (22.7 %) and postgraduates (14.7 %), while only 8 % had completed high school only. These figures demonstrate that the radio workforce is well educated, with the majority holding tertiary qualifications that equip them to understand and apply new technologies, such as artificial intelligence, in media production.
Figure 4. Job titles of radio professionals
Source: Authors
Job roles varied, though certain positions were more prominent. The largest groups were broadcasters (30.7 %), technicians (17.3 %), and news editors (13.3 %), followed by field reporters (14.7 %) and content producers (10.7 %). A smaller number held managerial, coordination, or specialized creative roles such as chief editor, program director, or field photographer. This mix shows that the survey captured voices from across the editorial, technical, and production spectrum, providing a balanced view of AI use from multiple professional perspectives within radio organizations.
Figure 5. Distribution of respondents by years of experience in radio
Source: Authors
The data show that almost half of the sample (49.3 %) had 10 or more years of professional experience, while about a quarter (25.3 %) had 5 to 9 years, and the same proportion had fewer than 5. This pattern indicates that the sector is staffed by long-term employees with substantial experience in traditional radio operations, which could influence both openness to innovation and the pace of AI adoption.
Descriptive Analysis for Independent Variables
AI Applications Usage: Descriptive Statistics (n = 75)
| Item | Min | Max | Mean | Std. Deviation |
|---|---|---|---|---|
| Are you personally capable of utilizing AI technologies in media production? | 1.00 | 5.00 | 3.4667 | 0.90544 |
| Does your station integrate AI technologies into its operations? | 1.00 | 5.00 | 3.1200 | 0.91474 |
| Text generation | 1.00 | 5.00 | 3.2133 | 1.14246 |
| News verification | 1.00 | 5.00 | 2.9867 | 1.15657 |
| News analysis | 1.00 | 5.00 | 2.8400 | 1.17450 |
Note: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.
The results indicate a moderate level of AI capability and integration within the sampled radio organizations. Respondents reported a fairly high self-perceived ability to utilize AI in media production (M = 3.47, SD = 0.91), suggesting that many journalists are becoming comfortable experimenting with generative and assistive tools. However, institutional integration remains slightly lower (M = 3.12, SD = 0.91), implying that newsroom adoption is still partial and often driven by individual rather than organizational initiatives. This pattern reflects an early diffusion stage in which AI use is common for personal productivity but not yet systematically embedded in production workflows.
Meanwhile, the results reveal moderate use of artificial intelligence applications across the examined newsroom functions. Text generation recorded the highest mean (M = 3.21, SD = 1.14), indicating that journalists increasingly rely on AI tools for drafting or assisting in content creation. This may reflect the growing accessibility of Arabic-language generative systems that simplify routine writing tasks.
In contrast, news verification (M = 2.99) and news analysis (M = 2.84) achieved lower means, showing that AI has not yet become integral to fact-checking or analytical reporting. These areas often require contextual reasoning and data reliability checks that exceed the current capability or trust level of available tools. The overall pattern suggests that AI adoption remains stronger in creative or linguistic assistance functions than in critical evaluation or interpretive tasks.
| Item | Min | Max | Mean | Std. Deviation |
|---|---|---|---|---|
| Faster news production | 1.00 | 5.00 | 3.8133 | 0.99585 |
| Improved news accuracy | 1.00 | 5.00 | 3.6400 | 1.06085 |
| Enhanced content quality | 1.00 | 5.00 | 3.7867 | 1.08171 |
| Efficiency in verification process | 1.00 | 5.00 | 3.6133 | 1.08918 |
Note: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.
The findings indicate positive perceptions of the benefits of AI integration within radio newsrooms. Faster news production obtained the highest means (M = 3.81, SD = 0.99), suggesting that respondents recognize AI’s significant role in accelerating workflows, particularly in drafting, editing, and scheduling content. Similarly, enhanced content quality (M = 3.79) ranked closely behind, implying that AI-assisted tools are viewed as improving the clarity, coherence, and overall professionalism of news outputs.
Meanwhile, improved news accuracy (M = 3.64) and efficiency in the verification process (M = 3.61) received slightly lower yet still favourable evaluations. This pattern suggests that while AI contributes to minimizing human error and streamlining fact-checking, journalists may remain cautious about depending entirely on automated systems for information validation. Overall, the results reflect a balanced optimism acknowledging tangible gains in speed and quality while recognizing persistent challenges in reliability and editorial trust.
| Lack of technical skills | 1.00 | 5.00 | 3.6933 | 0.97223 |
|---|---|---|---|---|
| Budget or financial constraints | 1.00 | 5.00 | 3.6000 | 1.05267 |
| Concerns about content credibility | 1.00 | 5.00 | 3.7733 | 0.89402 |
| Fear of job displacement | 1.00 | 5.00 | 3.5867 | 1.07921 |
Note: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.
The results reveal that respondents perceive multiple barriers hindering wider AI adoption in radio journalism, with concerns centered around credibility and capability issues. Concerns about content credibility registered the highest mean (M = 3.77, SD = 0.89), reflecting persistent scepticism toward the reliability of AI-generated outputs and apprehension about misinformation or loss of editorial integrity.
Lack of technical skills followed closely (M = 3.69), suggesting that limited staff training and insufficient digital literacy remain key obstacles to effective implementation. Budget or financial constraints (M = 3.60) and fear of job displacement (M = 3.59) were also moderately high, indicating that both resource limitations and professional insecurities shape attitudes toward AI tools. Overall, the pattern underscores that successful integration of AI in newsrooms requires not only financial investment but also capacity building and transparent editorial safeguards to maintain trust and morale.
| Item | Min | Max | Mean | Std. Deviation |
|---|---|---|---|---|
| AI will shape the quality of radio content | 2.00 | 5.00 | 4.0533 | 0.91376 |
| Staff will need advanced training on AI tools | 2.00 | 5.00 | 4.2933 | 0.78454 |
| AI will enhance creativity and innovation in radio work | 1.00 | 5.00 | 4.0400 | 1.05830 |
Note: 1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree.
The results demonstrate strong forward-looking optimism regarding the future role of AI in radio journalism. Staff will need advanced training on AI tools, which achieved the highest means (M = 4.29, SD = 0.78), indicating widespread recognition that continuous professional development and technical upskilling will be essential for maximizing AI’s potential. These findings highlight media practitioners' awareness that AI competence will soon become a core newsroom skill.
Similarly, high agreement levels for AI shaping the quality of radio content (M = 4.05) and AI enhancing creativity and innovation in radio work (M = 4.04) suggest that respondents anticipate AI as a transformative force across content production and creative processes. Together, these results point to a confident and progressive outlook, where journalists acknowledge AI not as a threat but as a catalyst for higher-quality, more dynamic, and technologically adaptive media practices.
3.2 Demographic differences in AI adoption
To further explore patterns of AI adoption among radio professionals, inferential statistical analyses were conducted to examine differences according to selected demographic characteristics. Independent-samples t-tests were used to assess gender differences, while one-way analysis of variance (ANOVA) was employed to examine variations across experience groups. The analyses focused on AI usage and perceptions of future AI prospects within Palestinian radio newsrooms.
| Variable | Female Mean | Male Mean | t | p |
|---|---|---|---|---|
| AI Usage | 3.34 | 2.78 | 2.739 | 0.008 |
| Future Prospects | 4.21 | 3.87 | 2.021 | 0.047 |
Female respondents reported significantly higher levels of AI usage (M = 3.34) than male respondents (M = 2.78), t(73) = 2.739, p = 0.008. Similarly, female participants expressed more optimistic expectations for the future role of AI in radio journalism (M = 4.21) than males (M = 3.87), t(73) = 2.021, p = 0.047. These findings suggest that female professionals in the sample demonstrate stronger engagement with AI technologies and a more positive outlook toward their future applications.
| Experience Group | n | Mean | SD |
|---|---|---|---|
| < 3 years | 10 | 3.63 | 1.11 |
| 3–4 years | 9 | 2.97 | 0.65 |
| 5–9 years | 19 | 3.07 | 1.05 |
| ≥ 10 years | 37 | 2.75 | 0.69 |
A one-way ANOVA revealed significant differences in AI usage across experience groups, F(3,71) = 2.869, p = .042. Respondents with less than three years of experience reported the highest level of AI usage (M = 3.63), whereas those with ten or more years of experience reported the lowest level (M = 2.75). These findings indicate that less experienced professionals are generally more inclined to adopt AI tools in their daily work.
| Experience Group | n | Mean | SD |
|---|---|---|---|
| < 3 years | 10 | 4.21 | 0.65 |
| 3–4 years | 9 | 4.47 | 0.39 |
| 5–9 years | 19 | 3.89 | 0.77 |
| ≥ 10 years | 37 | 3.86 | 0.68 |
No statistically significant differences were observed in perceptions of future AI prospects across experience groups, F(3,71) = 2.479, p = .068. Although respondents with 3–4 years of experience reported the most optimistic expectations regarding AI (M = 4.47), these differences did not reach statistical significance, indicating relatively consistent views across experience levels.
4. Discussion
AI uptake in the sampled radio newsrooms appears individual-initiated and task-delimited, with the highest penetration in text-centric editorial work, particularly generative writing. Evidence across newsroom settings shows that early AI adoption typically concentrates on routine content production and automated text work, especially automated reporting, summarization, and drafting, before organizations commit to deeper workflow redesign or system-wide integration (Ufarte-Ruiz et al., 2023; Banafi, 2024; Yeung & Dodds, 2024). In this study, the higher mean score for text generation (M ≈ 3.21) reflects this practical sequencing, as low-risk, desk-based tools are easier to pilot by individual staff without restructuring production routines or governance. The adoption profile therefore, signals an early-stage “trial-at-the-desk” pathway rather than a consolidated organizational transformation.
Perceived benefits in the dataset, including faster production, improved technical performance, and better content quality, align with research indicating that AI can raise efficiency by automating repetitive tasks and accelerating newsroom throughput when deployed as an assistive layer (Ayub, 2024; Talabi et al., 2024; Aziz, 2025). This benefit pattern is particularly plausible in radio contexts where speed, scripting, and rapid repackaging are central to daily production. At the same time, the findings indicate that respondents value human control over verification and editorial voice. Prior studies emphasize that credibility and accountability remain dependent on human oversight, especially to reduce bias, guard against misinformation, and manage ethical risks when AI outputs shape public-facing news content (Olanipekun & Olakoyenikan, 2022; Abdulrauf et al., 2025; Londoño-Proaño & Buele, 2025). The study’s results therefore, support an “assistive-but-verified” operational stance in which AI is used to draft, organize, or process information, while final editorial judgment and responsibility remain human-led.
Barriers identified in the sample —including shortages of technical expertise, limited budgets, and insufficient training— closely mirror those reported in developing, resource-constrained media systems. Empirical work shows that cost constraints, weak technical capacity, limited access to structured training, and unclear governance frameworks often prevent experimentation from maturing into standardized integration (Jamil, 2022; Kioko et al., 2022; Basak et al., 2024). However, these challenges should also be interpreted within the broader Palestinian media context. Palestinian radio stations operate under conditions shaped by the ongoing Israeli military occupation, in addition to economic, technological, and institutional constraints. Previous assessments of the Palestinian media sector have highlighted structural barriers to media development, including limitations on broadcasting frequencies, access to advanced communication technologies, and broader infrastructure challenges that may hinder digital transformation and institutional innovation (UNESCO, 2014). At the professional level, Palestinian journalists continue to work under conditions associated with prolonged conflict and operational restrictions that influence newsroom practices and organizational priorities (Hazboun et al., 2019). More recent assessments have also highlighted persistent pressures on Palestinian media institutions, including resource limitations, professional challenges, and the need to strengthen institutional capacity in a rapidly evolving digital environment (PJS, 2024). Furthermore, local radio stations in the West Bank face institutional and sustainability challenges, including limited resources and commercial pressures that affect their ability to invest in technological development and innovation (Heywood, 2018). The prominence of ethical uncertainty in the findings is also consistent with research noting that regulatory ambiguity and the absence of internal guidelines can discourage institutional adoption and keep AI use informal, uneven, and difficult to audit (Al-Zoubi et al., 2024; Vincent et al., 2025). Within this context, the moderate level of AI adoption observed in the present study may reflect not only organizational readiness but also the broader structural conditions affecting Palestinian radio stations. These factors help explain why AI adoption remains largely individual-driven rather than institutionally embedded as a coordinated newsroom capability.
The inferential results further suggest that AI adoption is not evenly distributed among newsroom professionals. Female respondents reported significantly higher levels of AI use and more optimistic expectations regarding future AI integration, while less experienced professionals demonstrated greater engagement with AI tools than their more experienced counterparts. These findings may indicate that openness to technological innovation is associated with both professional background and individual attitudes toward digital transformation. The results also suggest that younger, less experienced media professionals may be more willing to experiment with emerging technologies, whereas more experienced practitioners may adopt AI more cautiously due to established professional routines and concerns about editorial responsibility.
Forward-looking expectations reveal strong agreement that advanced AI training will be necessary and that journalists and presenters will increasingly rely on AI tools. This aligns with studies showing that newsroom professionals may hold generally positive attitudes toward AI but still lack the literacy and structured preparation needed to apply tools safely, evaluate outputs, and integrate them into editorial routines (Sharadga et al., 2022; Basak et al., 2024; Abdulrauf et al., 2025). The prominence of training in the results suggests that respondents recognize skills as the main bottleneck separating perceived usefulness from consistent and accountable practice. Recommendations in the literature converge on the need for newsroom-level capacity building, clear ethical guidelines, and supportive regulatory frameworks as prerequisites for sustainable AI integration (Olanipekun & Olakoyenikan, 2022; Vincent et al., 2025).
The findings are also consistent with evidence from other Arab media environments. Studies conducted among Egyptian journalists and within the Iraqi Media Network reported generally positive perceptions of AI’s ability to improve newsroom efficiency, content quality, and journalistic performance, while simultaneously highlighting concerns related to ethical responsibility, misinformation, algorithmic bias, and the preservation of human editorial oversight (Mohialden et al., 2025; Okela et al., 2026). Similar patterns emerged in the present study, where respondents acknowledged the benefits of AI in enhancing production efficiency and content quality while emphasizing the continued importance of human oversight in verification and editorial decision-making. These similarities suggest that Palestinian radio stations share many of the opportunities and challenges observed across the wider Arab media landscape.
Finally, broader research on AI’s gradual but transformative influence on newsroom culture supports interpreting these findings as a transitional phase rather than a stable end state. Studies argue that AI reshapes workflows and professional roles incrementally, particularly in semi-automated environments where human editorial authority is retained while selected tasks are partially automated (Moran & Shaikh, 2022; Owsley, 2023). Taken together, the findings indicate high perceived utility but conditional institutional readiness. Movement from assistive trials to systematic transformation in these radio newsrooms will likely depend on sustained investment in training, governance, infrastructure, and robust human oversight.
5. Conclusions
This field-based survey examined AI adoption in Hebron radio newsrooms across 14 stations (n = 75), focusing on AI use for news generation and verification. Overall adoption is moderate, with the strongest uptake in text-centered editorial work, particularly generative writing (M ≈ 3.21). This indicates that AI use is currently desk-based and individually initiated, rather than embedded as a coordinated newsroom system.
With respect to OB1, the findings show that AI tools are used most intensively for drafting, rewriting, and summarization, while verification-oriented uses remain less developed. The observed pattern suggests that staff prioritize low-barrier applications that deliver immediate productivity gains in routine editorial work.
Regarding OB2, AI integration appears selective and task-delimited rather than fully workflow-integrated. Use tends to occur at the individual level without standardized newsroom procedures for input documentation, source verification routines, or consistent quality-control checkpoints, which limits the possibility of systematic implementation.
About OB3, participants report clear perceived benefits—especially faster production, improved technical performance, and better content quality. At the same time, the results imply an “assistive” role for AI: tools support drafting and processing, while final editorial judgment and verification remain human-led, reflecting ongoing concerns about credibility and accountability.
Concerning OB4, the main obstacles cluster around limited technical expertise, insufficient training opportunities, budget constraints, and gaps in ethical and organizational guidance. These constraints help explain why adoption remains uneven across roles and stations and why institutionalization is still limited.
Taken together, the findings suggest that AI adoption in Palestinian radio newsrooms remains at an early institutionalization stage, where usage is still driven primarily by individual initiatives rather than formal organizational strategies. Although perceptions of AI usefulness are generally positive, organizational readiness remains conditional and uneven across stations. Moving from individual experimentation to sustainable integration will require structured training, shared infrastructure, and explicit governance measures, including verification protocols, accountability procedures, and quality standards.
From a policy perspective, the findings highlight the need for Palestinian media institutions, professional associations, and regulatory bodies to support AI adoption through capacity-building programs, ethical guidelines, and technical infrastructure development. Establishing clear institutional policies for AI-assisted journalism may help ensure that technological innovation enhances newsroom performance while maintaining professional standards, transparency, and public trust.
The results should be interpreted within the limits of a descriptive survey and self-reported perceptions in a specific local setting. Future research can strengthen the evidence base by incorporating observational workflow analysis and measurable performance indicators, such as verification accuracy, production time, operational efficiency, and cost management.
6. Support
DesinfoperIA Journalistic applications of AI to mitigate disinformation: trends, uses and perceptions of professionals and audiences. Reference: PID2023-147486OB-I00, funded by the Spanish Ministry of Science and Innovation.
gen-IA: Artificial Intelligence Tools Library for Content Creation in News Media. Reference: JA.B3-23. PPRO-B3-2023-03. Funded by the University of Málaga.