Reframing tour guiding in the age of generative AI: a framework for self-guided tourism experiences

The exponential development of generative artificial intelligence (GAI) technologies is transforming processes across various industries, including healthcare, education, and customer service (Gursoy et al., 2023). Within the tourism sector, GAI has the potential to revolutionize how travelers interact with destinations by offering real-time, contextually rich, and highly personalized information (Mladenović et al., 2024a, b). Positioned as a digital support layer for independent travel, GAI may help travelers design and navigate experiences with greater responsiveness, informational autonomy, and convenience (Wong et al., 2023). This growing technological shift aligns with the increasing demand for self-guided experiences (SGE), a market segment that is expanding significantly. Recent studies indicate that approximately 73% of global travelers prefer to plan and explore destinations independently, utilizing digital tools and online resources (Banerjee and Chua, 2020). This trend highlights the growing importance of autonomous, technology-supported tourism experiences in meeting evolving traveler expectations.

Traditionally, human tour guides have served as the cornerstone of immersive tourism, offering interactive storytelling, cultural insights, and emotional connections that elevate travelers' understanding of a destination (Wang et al., 2023). However, human-guided tours often come with practical limitations, including scheduling rigidity, variable quality, language barriers, and accessibility issues (Quang et al., 2024). For instance, a report by Lee and Lee (2025) estimates that language barriers hinder up to 30% of international travelers from fully engaging with guided experiences, while rigid schedules often prevent tourists from exploring destinations at their preferred pace. As travelers increasingly prioritize independence and adaptability (Li et al., 2020), these challenges emphasize the need for alternative solutions that can deliver real-time and personalized experiences. GAI, with its ability to process vast datasets and respond to travelers' inquiries instantaneously (Carvalho and Ivanov, 2024), has the potential to meet these needs.

Despite its potential, the role of GAI in facilitating SGE remains underexplored in the existing literature. While research has addressed aspects of GAI adoption in tourism, such as its use in trip planning, digital platforms, and travel marketing (e.g. Carvalho and Ivanov, 2024; Gursoy et al., 2023; Guttentag et al., 2024; Xu et al., 2024; Zhang and Prebensen, 2024), there is a need to conceptualize GAI-enabled SGE as a distinct phenomenon. Self-guided tourism, driven by travelers' desire for flexibility and tailored engagement, is expected to grow rapidly (Li et al., 2020). Forecasts suggest that the global market for self-guided travel technology will reach $35 billion by 2030, reflecting the demand for innovative solutions that support autonomous exploration (Huang et al., 2026). However, questions regarding GAI's ability to deliver cultural sensitivity, emotional engagement, and spontaneous adaptability remain unanswered (Hu and Min, 2023). Additionally, there is little understanding of how these tools can bridge the gap between static informational resources and interactive human guidance to enhance SGE (Wang et al., 2023).

This conceptual paper addresses this gap by proposing a conceptual framework for GAI-enabled SGE. By focusing on the interplay between personalization, real-time assistance, and contextual awareness, the study highlights the strengths and limitations of GAI in delivering autonomous and adaptable travel experiences. Furthermore, it examines the implications of GAI adoption for key stakeholders, including tourists, tourism service providers, and destination management organizations. The need to conceptualize SGE is especially pressing given the rapid technological advancements and, more importantly, shifting traveler expectations and habits. For example, reports indicate that 65% of millennial and Gen Z travelers prefer personalized itineraries and digital tools that allow them to explore destinations at their own pace (Bilynets et al., 2023). Self-guided tourism, facilitated by GAI, may redefine travel autonomy, allowing tourists to engage with destinations in ways that are contextually appropriate. However, for these benefits to be fully realized, it is essential to develop a clear understanding of how GAI can enable and enhance SGE.

This paper makes a conceptual contribution by clarifying the role of GAI in self-guided tourism experiences. Although GAI is increasingly used in travel-related services, its specific role in supporting, extending, or partially reconfiguring selected guiding functions remains insufficiently defined. By identifying personalization, real-time assistance, and contextual awareness as three core functionalities of GAI-enabled SGE, the paper explains how tourists may access, interpret, and apply destination information in more autonomous and context-responsive ways. At the same time, the paper does not frame GAI as a replacement for tour guiding as a profession. Tour guiding involves emotional labor, cultural mediation, embodied interaction, and regulatory embeddedness that extend beyond information provision. The contribution of this paper is therefore not to present GAI as an alternative to human guiding, but to conceptualize how GAI may reshape selected information-based and logistical functions within self-guided tourism.

This study adopts a descriptive and explicating approach (Kindermann et al., 2024), consistent with MacInnis' (2011) framework for conceptual contributions. A descriptive analysis is important in this context, given the conceptual novelty and fragmentation in current literature surrounding GAI-enabled tourism. By synthesizing existing knowledge, we aim to develop a coherent framework that captures the unique features and implications of GAI in facilitating SGE. Initially, we reviewed approximately 105 relevant scholarly sources published over the last decade to ensure a comprehensive view of trends and developments. This extended time frame allowed us to capture any preliminary insights and track the evolution of key concepts related to GAI, information retrieval, and tourism. Following this broad literature collection, we conducted a rigorous screening process to refine the selection to studies directly related to our research focus. The refined set of articles focused on core themes, including GAI, on-site information-seeking and retrieval, tourism, hospitality, and the unique features of SGE. After narrowing down to the most relevant works, we integrated the existing knowledge to conceptualize “GAI-driven SGE,” where GAI serves as an active agent in information provision, tailored for diverse travel phases (pre-travel, on-site, and post-travel) and modes (searching, sharing, and applying). The final step involved examining how GAI-driven information experiences influence tourists' engagement with on-site information across travel phases and affect key dimensions such as information-seeking behavior, decision-making, and interaction with local environments. This conceptualization emerged from examining real-world scenarios where tourists rely on digital tools for independent, non-guided travel, while also offering insights into potential benefits, limitations, and implications for stakeholders in the tourism and hospitality sectors.

Tour guides play a vital role in enhancing the tourism experience, offering more than just information about destinations (Li et al., 2021). Their role is multidimensional, combining information provision, interpretation, coordination, emotional engagement, cultural mediation, visitor management, and the facilitation of responsible tourist behavior. Recent research continues to show that tour guides shape tourists' experiences not only through factual knowledge, but also through communicative competence, storytelling, professionalism, interpersonal sensitivity, and the ability to translate local meanings into visitor-relevant narratives (Wang et al., 2023; Quang et al., 2024; Kul et al., 2024; Leong et al., 2024; Aideed et al., 2025). Beyond simply sharing facts, tour guides interpret local history, culture, and traditions through storytelling, providing travelers with a richer understanding and connection to their surroundings (Choi et al., 2007; Li et al., 2020; Li et al., 2021). Historical storytelling, in particular, can strengthen tour-guide interaction, perceptions of authentic place, and tourists' educational, entertainment, experiential, and emotional value in cultural heritage contexts (Leong et al., 2024). This interpretive and relational skill set makes human guides especially important for tourists who seek deeper, more immersive, and culturally situated engagement with the places they visit. One of the core strengths of human tour guides lies in their expertise (Wang et al., 2023), which combines formal training, professional competence, personal experience, and local insight. With a profound knowledge of the area, guides can contextualize information, making it accessible and meaningful for diverse groups of tourists. Recent studies further suggest that tour-guide competence and professionalism influence cultural tour experience, visitor satisfaction, behavioural intentions, engagement, and sustainable visitor behavior, confirming that guiding is not only an informational service but also a behavioural and experiential intervention (Syakier and Hanafiah, 2022; Kul et al., 2024; Alazaizeh et al., 2019; Aideed et al., 2025). Their storytelling abilities allow them to transform standard tours into immersive experiences, where historical facts and cultural significance are anchored into narratives that resonate on a personal level (Choi et al., 2007; Leong et al., 2024). This interpretive skill enables tourists to move beyond mere sightseeing, deepening their appreciation of the destination and leaving a lasting impression.

Human interaction is another essential aspect that tour guides bring to the tourism experience. The dynamic exchange between guides and tourists fosters a unique and personalized experience because guides can respond to verbal and non-verbal cues, adapt their pacing, adjust their commentary, and manage group dynamics in real time. Recent work on heritage walking tours also shows that guides act as cultural interpreters, coordinators, image makers, and mediators of the tourist encounter, further demonstrating that their role extends well beyond the delivery of destination information (Tahir et al., 2025). This responsiveness allows guides to create a sense of connection with visitors, making the tour more enjoyable and relevant. Importantly, these relational, performative, and culturally situated aspects of guiding are central to the distinction developed in this paper. While GAI may support or selectively assume some information-related and logistical functions, it cannot straightforwardly replicate the embodied, interpretive, emotional, and socially accountable dimensions of human tour guiding.

However, traditional tour guides also face some limitations. Notably, language barriers can impede communication with international tourists (Seyitoğlu, 2020), and scheduling constraints may restrict access for travelers who cannot attend group tours at set times. Additionally, the quality of the tour experience can vary based on a guide's experience, training, and personal style, leading to potential inconsistencies (Banerjee and Chua, 2020). The need for an in-person presence may also limit accessibility for those with mobility challenges or specific requirements, making traditional guided tours less inclusive for certain audiences (Liu et al., 2024). While human tour guides bring invaluable knowledge, interactive engagement, and adaptability to the individuals' experience, practical challenges persist. These challenges have paved the way for exploring alternative solutions, such as GAI, to support or supplement the traditional role of tour guides in modern tourism, especially in enhancing accessibility and inclusivity.

While GAI is often described in terms of its technical capabilities, its integration into tourism must also be viewed through a broader social and institutional lens (Sigala et al., 2024). At present, the benefits of GAI adoption in tourism are unequally distributed (Wang et al., 2023; Zhang and Prebensen, 2024). Tourists, particularly those with high digital literacy, enjoy personalized, real-time information and flexible navigation. Tech platforms and global software providers gain access to large-scale data, which supports monetization and market expansion. However, these advantages contrast sharply with the challenges faced by traditional tourism workers, as GAI takes on roles related to interpretation, navigation, and itinerary planning. Without clear strategies for integrating these professionals into AI-supported models, many may face declining relevance or income insecurity. Beyond labor impacts, GAI introduces complex ethical and cultural considerations (Dwivedi et al., 2023). AI-generated content may unintentionally reflect biases or inaccuracies in representing local customs, particularly when training data lacks cultural sensitivity (Kim et al., 2023). The risk of cultural misrepresentation or simplification becomes especially relevant in heritage-rich destinations where authenticity is key to the experience. Additionally, as GAI tools rely on real-time geolocation, behavioral data, and language processing, concerns over privacy and consent emerge – raising questions about how personal data is collected, stored, and used in tourism settings (Carvalho and Ivanov, 2024).

Professionals in the sector are beginning to respond to these challenges, though efforts remain early-stage. Destination Management Organizations (DMOs) are experimenting with partnerships that embed local knowledge into GAI or encourage hybrid models where human guides complement GAI tools (Grundner and Neuhofer, 2021). Others are advocating for AI-literacy programs and ethical standards to ensure that GAI aligns with destination values and sustainability goals (Hsu et al., 2024). There is also growing discussion about the need for legal safeguards (Dogru et al., 2023), particularly in areas such as liability for misinformation and platform accountability (Zhang et al., 2025). Yet, despite these emerging initiatives, systematic support at the institutional and policy level is still limited (Jo, 2023; Kshetri et al., 2024), and debates around equity, representation, and labor protection are often overshadowed by a techno-optimistic narrative. Altogether, this perspective emphasizes that GAI in tourism is not just a technology – it is a socially shaped system with winners and losers, embedded in broader questions of power and governance. As GAI becomes more integrated into the tourism experience, it is crucial to examine not only what these systems can do but also how they are introduced, who controls them, and what social consequences they bring (Dwivedi et al., 2023; Mladenović et al., 2024a, b; Sigala et al., 2024). These issues are central to understanding the long-term implications of GAI for inclusive and responsible tourism.

As the adoption of GAI accelerates, the ethical and practical consequences of its deployment must be addressed. One core concern involves the potential for cultural distortion or misrepresentation (Sigala et al., 2024; Wu et al., 2025). GAI relies on datasets that may underrepresent or inaccurately portray local customs, leading to unintended consequences when generating travel recommendations (Ivanov, 2023). In destinations where cultural protocols are central to the visitor experience, such inaccuracies risk encouraging inappropriate behavior, thereby harming host-guest relations and cultural preservation efforts.

Furthermore, questions of algorithmic transparency and data governance remain largely unresolved (Dwivedi et al., 2023). Tourists frequently rely on GAI-generated content without understanding how recommendations are derived or what data is used in the process (Mladenović et al., 2024a, b). This opacity raises issues related to user consent, data provenance, and the potential for manipulation or bias in tourism promotion. Establishing clear standards for explainability and accountability in GAI-driven services is fundamental, particularly when these systems influence on-site decisions with material impacts on individuals and communities. Another ethical challenge concerns the displacement of labor within the tourism workforce (Dogru et al., 2023). Although GAI enhances efficiency and personalization, it also introduces pressures on traditional service roles (most notably, tour guides and travel advisors). The shift toward AI-mediated experiences may reduce demand for these roles, especially in low-income destinations where tourism is a key source of employment. A socially responsible transition will require proactive workforce reskilling and policies that support inclusive innovation. Practically, uneven AI literacy poses further challenge, as travelers from marginalized groups or less technologically developed regions may face barriers to using GAI-enhanced platforms (Li et al., 2025), potentially inducing digital divides in access to information. Ensuring that GAI tools are inclusive and accessible will be vital to fulfilling their promise of democratizing travel. Ultimately, the integration of GAI into tourism systems must be guided by cross-sector dialog. Designers, developers, policymakers, and community stakeholders each must play a role in ensuring that technological advancement does not come at the expense of social and cultural integrity.

The incorporation of GAI into self-guided tourism is recognized for its capacity to enhance traveler autonomy, customization, and engagement. Existing theoretical frameworks, such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), underline the importance of perceived ease of use, perceived usefulness, and trust in shaping user receptivity (Venkatesh et al., 2011). These factors are not experienced uniformly but rather are filtered through a range of socio-demographic, cultural, and experiential variables that influence individual interaction with emerging technologies. Rather than displacing human-mediated tourism experiences, GAI appears to operate as an adaptive tool that may support or extend traditional forms of engagement. For travelers with high levels of digital proficiency, such systems may offer a more autonomous mode of navigation and discovery. For others, they function as complementary resources, providing just-in-time assistance (e.g. translation, contextual recommendations, or logistical guidance). This variability in usage emphasizes the potential of GAI to accommodate diverse user needs without imposing a singular model of interaction (Seyfi et al., 2025). While improvements in digital infrastructure have reduced barriers to participation, disparities in digital literacy continue to shape how users engage with AI. Ensuring broad usability across groups, linguistic backgrounds, and cultural contexts requires thoughtful design. By addressing such considerations proactively, GAI applications in tourism may contribute not only to more personalized experiences but also to more inclusive forms of travel.

GAI may reshape selected information-based functions within tourism by offering real-time, contextually relevant, and user-responsive support (Dogru et al., 2023). In self-guided tourism, it can act as a digital support layer that assists tourists with information retrieval, translation, itinerary adjustment, navigation, and on-site decision-making (Mladenović et al., 2024a, b). This does not mean that GAI replaces tour guides as embodied professionals. Rather, GAI may supplement, redistribute, or selectively assume specific informational and logistical functions traditionally associated with guiding. Its relevance lies in the ability to provide scalable and adaptive information support, while its limitations remain evident in areas requiring cultural judgment, emotional intelligence, ethical accountability, and situated interpretation.

Notably, one of the most promising applications of GAI in tourism lies in on-site information retrieval (Dwivedi et al., 2023; Mladenović et al., 2024a, b; Wong et al., 2023). Embedded in mobile applications, GAI may theoretically deliver location-specific content, historical context, and practical guidance. Integrating GAI with GPS further enhances this utility, enabling the AI to deliver information that is tailored not only to the tourist's current location but also to their specific interests (Paul et al., 2023). For instance, tourists standing near a landmark can receive immediate, in-depth information on its significance, as well as logistical details on nearby facilities or recommendations for further exploration. In this way, GAI may transform smartphones into interactive guides that adapt in real-time to the traveler's preferences. Compared to traditional tours, GAI offers several unique advantages as it provides scalability, enabling consistent access to high-quality information for tourists regardless of group size, season, or location (Sigala et al., 2024). Additionally, GAI supports multiple languages, making it accessible to a global audience and overcoming language barriers that often challenge human guides. Personalized recommendations allow tourists to explore destinations at their own pace, tailoring the information flow to suit their interests and energy levels (Ren et al., 2024). This adaptability is especially advantageous for some particular user groups (e.g. seniors).

While GAI brings notable strengths in scalability, accessibility, and personalization, it also presents limitations when compared to the depth, adaptability, and emotional connection offered by human tour guides (e.g. Christou et al., 2020; Wang et al., 2023). To fully understand this interplay, it is important to explore their strengths and challenges in terms of information quality, personalization, interactivity, and accessibility. GAI excels in delivering extensive, factual information, and its ability to provide real-time responses ensures that tourists can access context-specific information on demand (Sigala et al., 2024). This makes GAI a powerful tool for travelers seeking information about historical landmarks, cultural practices, or logistical details. However, GAI responses often lack the depth and authenticity that come from lived, local experience – a hallmark of tour guides (Wang et al., 2023). Human guides bring emotional storytelling and cultural complexity to their explanations, offering a richer, more engaging context that fosters a deeper connection with the destination. In contrast, GAI's approach can sometimes feel impersonal or detached, limiting its ability to replicate the narratives that define human-guided tours.

Personalization is another important area where GAI demonstrates growing potential, as GAI can adapt its responses to individual preferences, tailoring the information to align with a traveler's specific interests (Mladenović et al., 2024a, b). This level of customization makes it particularly appealing for independent travelers who value flexibility (Mouritzen et al., 2024). However, GAI may lack the empathetic engagement that human guides bring to their interactions. Human guides can dynamically adjust their delivery based on group dynamics (Quang et al., 2024; Ren et al., 2024), respond to non-verbal cues, and build rapport with travelers. This interpersonal adaptability not only enhances the quality of the tour but also creates a sense of connection and engagement that GAI cannot replicate. Notably, GAI offers cost-effective solutions (Miao and Yang, 2023), making high-quality information accessible to a range of tourists, including those who cannot afford private or group tours. Its 24/7 availability and lack of logistical constraints provide unmatched convenience for travelers. Yet, this reliance on digital tools assumes a level of technological literacy that not all tourists possess, potentially excluding those who are less comfortable with such systems (Weed, 2023). Additionally, travelers who value the relational aspects of tourism, such as face-to-face interactions, may find AI solutions lacking in emotional context. A thorough comparison between traditional and SGE is presented in Table 1.

Self-guided tourism refers to a mode of travel where tourists independently plan, navigate, and engage with destinations without the direct assistance of a tour guide (MacLeod, 2016). These experiences are characterized by the autonomy they provide, allowing travelers to explore at their own pace and according to their interests. Notably, GAI may become an important enabler of SGE by offering advanced tools that provide real-time, context-specific, and personalized recommendations. Unlike static resources (e.g. guidebooks) (Li et al., 2020), GAI transforms devices into interactive tour guides, capable of adapting to the traveler's location, preferences, and inquiries. With its ability to deliver coherent, on-demand responses, GAI may enhance SGE by bridging gaps in knowledge, convenience, and adaptability. For instance, a traveler visiting Berlin might inquire about the history of the Brandenburg Gate and instantly receive information along with nearby, less-crowded sites of historical importance. Similarly, GAI could suggest quieter sights for travelers hoping to avoid the congestion. This ability to redistribute tourist traffic highlights GAI's potential to mitigate over-tourism (Sigala et al., 2024) by directing travelers to alternative, equally enriching experiences, reducing pressure on overburdened landmarks while promoting lesser-known attractions. Similarly, GAI can also address the challenges of peak-season travel. For example, a tourist in Santorini who finds the main sunset viewing points overcrowded might use GAI to discover an alternative location with an equally stunning view. Such capabilities not only improve the experience but also contribute to sustainable tourism by spreading visitors more evenly across locations.

Figure 1 presents the conceptual framework of GAI-enabled SGE. The framework is organized around three core functionalities: personalization, real-time assistance, and contextual awareness. These dimensions are derived from prior work on consumer decision-making, AI-human collaboration, service interaction design, and self-guided tourism (Shang et al., 2023; Mladenović et al., 2024a, b; Wang et al., 2023; MacLeod, 2016; van Dis et al., 2023; Bulchand-Gidumal et al., 2024). We do not claim that these functionalities are exhaustive. Rather, they capture the GAI affordances most relevant to autonomous, adaptive, and context-responsive tourism experiences. Importantly, their effects are not purely technical or uniformly positive. They are shaped by stakeholder interests, destination infrastructure, governance arrangements, cultural norms, and user capabilities. This structure is consistent with research on sociotechnical systems, where technology features are analyzed in terms of how they mediate perception, action, and interaction in real-world contexts. These features, situated within a broader socio-technological environment, operate through a GAI integration that supports adaptive decision-making and information engagement. Notably, the framework incorporates key moderators, such as connectivity, digital literacy, interface quality, user trust, and destination infrastructure, which influence the effectiveness of GAI deployment. Furthermore, tourist profile functions as a higher-order moderator shaping how travelers engage with SGE depending on their preferences, capabilities, and motivations. Outcomes (e.g. enhanced autonomy, reduced dependency on human guides, improved interaction, and overall satisfaction) reflect the benefits of well-functioning GAI-enabled systems. The model therefore embeds the three core functionalities within a broader socio-institutional environment shaped by stakeholder interests (including tourists, DMOs, tour guides, platform providers, and public authorities), ethical constraints, and governance conditions. These elements do not merely surround the framework; they condition how GAI is used, what kinds of outcomes are prioritized, and how value and authority are redistributed across the tourism ecosystem. Cultural norms, represented as a foundational layer, modulate how GAI features are implemented and interpreted across different travel contexts.

Taken together, personalization, real-time assistance, and contextual awareness represent analytically distinct but interrelated functionalities of GAI-enabled SGE. The following subsections discuss each functionality separately, together with its principal value and associated trade-offs. For this paper, each feature is classified into three levels (low, medium, high), based on its anticipated influence on user experience and technological complexity, informed by prior work in tourism technology (e.g. Buhalis and Moldavska, 2022; Liu et al., 2024). The classification reflects the following conceptual assumptions:

1. Low – the feature is largely absent (e.g. personalization may be limited to generic suggestions while contextual awareness may rely on pre-loaded content).

2. Medium – the feature is partially functional or conditionally present. (e.g. real-time assistance may work only in urban centers with strong connectivity).

3. High – the feature is fully integrated and responsive, typically requiring advanced data processing and AI support (continuous personalization based on real-time behavioral inputs and dynamic context sensing).

The specific classification assigned to each scenario (S1–S8) is based on combinations of these levels, guided by both conceptual logic (e.g. user needs in high-flexibility vs. safety-first contexts) and currently known capabilities of GAI. Rather than predicting future technological states, the purpose of this framework is to map out conceptual possibilities to better understand how GAI may reshape the structure and quality of SGE under different design conditions.

Personalization is central to SGE because it addresses travelers' demand for autonomy, efficiency, and meaningful engagement (Wang et al., 2023). Personalization has been studied in recommender systems (Chandra et al., 2022; Dhananjaya et al., 2024; Vullam et al., 2023), where it supports user engagement by tailoring content to past preferences, declared goals, or inferred traits. Unlike traditional guided tours, which often follow a generic “one-size-fits-all” approach (Shang et al., 2023), personalized GAI holds the potential to enable travelers to adjust experiences that align with their interests, preferences, and circumstances. This capacity for personalization ensures that self-guided tourists can maximize the relevance and satisfaction of their travel experiences without requiring exhaustive pre-trip research or rigid adherence to prearranged schedules. One of the most important benefits of personalization is its role in enhancing decision-making (Kim et al., 2025). Travelers often face the challenge of “choice paralysis”, overwhelmed by the sheer number of options available in popular destinations (Mladenović et al., 2024a, b). To overcome this, GAI curates targeted options tailored to the traveler's interests, enabling more efficient decision-making (Dellaert et al., 2020). For instance, a history enthusiast in Athens may receive recommendations for lesser-known landmarks, allowing for deeper cultural immersion while avoiding overcrowded attractions. This targeted guidance also ensures meaningful engagement with the destination. Beyond decision-making, personalization also broadens the inclusivity and accessibility of self-guided tourism by catering to diverse traveler needs (Wang et al., 2023). For families visiting Tokyo, GAI might suggest child-friendly destinations, while for solo travelers, it could recommend social or community-oriented activities like local cooking classes or art workshops. This ensures that SGE tourism is accessible to a broader demographic, addressing various travel motivations and expectations (Hsu et al., 2024). Perhaps the most compelling feature of personalization is its dynamic adaptability. Unlike traditional static itineraries, GAI tools may recalibrate in real-time, responding to changes in circumstances or interests. For example, if a traveler in Florence chooses to spend additional time at the Uffizi Gallery, the GAI may propose nearby venues for lunch or adjust afternoon plans to include alternative landmarks. This adaptability ensures that the experience remains relevant and engaging, even when unexpected changes arise (Tu et al., 2020). Ultimately, personalization may enhance perceived relevance and engagement, although its benefits depend on how transparently and responsibly it is implemented.

While personalization enables tailored experiences, it also raises concerns related to data privacy, algorithmic profiling, and potential filter bubbles. Hypothetically, “over-personalization” may limit spontaneous discovery or reinforce narrow behavioral patterns, especially if users are unaware of how their preferences are being shaped.

The real-time feature of GAI plays an emerging role in addressing the fluid and unpredictable nature of SGE tourism (MacLeod, 2016). By delivering immediate and relevant recommendations, GAI allows travelers to navigate dynamic environments with confidence (Mladenović et al., 2024a, b). Real-time functionality connects to literature on adaptive systems and just-in-time information delivery (Behravan and Gracanin, 2024; Bilal et al., 2025; Brasoveanu et al., 2020), which enhances user responsiveness during information-rich tasks such as navigation and planning. This ability is vital for self-guided tourists, who often face logistical and situational challenges without the support of a human guide. One of the most significant contributions of real-time assistance may be its ability to manage uncertainty (van Dis et al., 2023). Tourism inherently involves unpredictability, such as sudden closures, weather disruptions, or navigation difficulties, which can lead to stress and dissatisfaction (Bulchand-Gidumal et al., 2024). By enabling seamless adaptation, real-time assistance should enhance the overall experience (Carvalho and Ivanov, 2024). For example, a tourist in New York City might receive real-time updates about delays at the Metropolitan Museum of Art and be directed to nearby alternatives. This adaptability ensures that tourists can make the most of their time while minimizing disruptions. Moreover, real-time assistance enhances cultural immersion through instant language translation tools. Language barriers often limit tourists' ability to interact with locals or understand signage (Tu et al., 2020), reducing the authenticity of their experiences. GAI-powered translation features bridge this gap, enabling seamless communication. Lastly, real-time assistance provides responsive problem-solving to handle disruptions (Wong et al., 2023). For instance, a couple on a culinary tour might discover that a recommended restaurant is fully booked. GAI can instantly provide alternative dining options, complete with live reviews and availability, ensuring the experience is not compromised. This ability to adapt plans dynamically may help travelers maintain greater control, even in unforeseen circumstances.

Notably, although real-time assistance enhances responsiveness and flexibility, it may also lead to increased dependence on GAI. Tourists may come to rely heavily on automated guidance, reducing their capacity for independent decision-making or problem-solving in unfamiliar settings.

Contextual awareness is another important capability of GAI, enabling it to adapt to environmental, cultural, and other situational factors. By integrating live data from diverse sources (GPS, weather, user preferences, local norms), context-aware GAI ensures that travelers receive guidance that is not only timely but also situationally appropriate (Kshetri et al., 2024). While personalization is user-centered and profile-based, contextual awareness is situational and environment-based. It refers to the system's ability to respond to external, real-time cues such as location, time, environmental conditions, and cultural norms. In human-computer interaction, this aligns with the concept of context-aware computing (Amos and Zhang, 2024; Longoni and Cian, 2022; Pereira et al., 2024), where systems adapt based on situational input rather than internal user data. This understanding is important for travelers who rely on GAI as their primary source of support. One of the key advantages of contextual awareness is its ability to optimize decision-making under uncertainty (van Dis et al., 2023), a rather frequent challenge in tourism (Sigala et al., 2024). Research shows that decontextualized information often leads to suboptimal choices and reduced satisfaction (Boes et al., 2016), while hypothetically, the ability to integrate real-time environmental data helps travelers avoid frustrations and make the most of their experience. Another crucial role of contextual awareness is in promoting cultural sensitivity and appropriateness (Dwivedi et al., 2023). GAI systems can incorporate local norms and etiquette into recommendations, fostering respectful interactions between tourists and host communities. For instance, a visitor to a Thai temple might receive reminders about appropriate attire or behavior, ensuring that their presence aligns with cultural expectations. This not only enhances the traveler's experience but also supports sustainable tourism practices by minimizing cultural disruptions (Mandić et al., 2023).

Contextual awareness is also expected to contribute to safety and traveler well-being, particularly in outdoor or high-risk environments, by enabling timely warnings and route adjustments. For example, a hiker in the mountains might receive real-time alerts about trail closures or adverse weather conditions. Such a design may help mitigate risks and support traveler well-being (Li et al., 2021). Finally, contextual awareness can align recommendations with individual user states, such as fatigue or stress. By analyzing input patterns or wearable data, GAI could adjust itineraries to suit the traveler's current condition. For instance, a tired traveler might be directed to rest instead of a physically demanding activity. This alignment ensures that the journey remains enjoyable – enhancing satisfaction (Kim et al., 2025). Contextual awareness promises situational relevance, but its implementation depends heavily on data inputs that may lack cultural aspects or local authenticity. There is also a risk that algorithmically generated content flattens cultural diversity, replacing situated interpretation with generalized recommendations.

Each included dimension contributes differently to the quality of the SGE and reflects a unique role that GAI can play in supporting tourists (Kshetri et al., 2024; Mladenović et al., 2024a, b; Tassiello et al., 2024). Together, they form the basis of our descriptive framework, which aims to explain how and under what conditions GAI may enhance or limit the SGE. To comprehend the complex relations between personalization, real-time assistance, and contextual awareness, we composed Figure 2 and Table 2. The scenarios are illustrative in nature as they are not derived from empirical testing but are based on existing literature and known functionalities of GAI. Their purpose is to map out potential use cases and, as such, the implications drawn from these examples should be interpreted as theoretical possibilities rather than evidence-based findings.

While these scenarios are illustrative, they serve as conceptual anchors for generating future research directions. Rather than treating them as predictive claims, we distill from them a set of propositions that outline how GAI might reshape SGE under specific conditions. For instance:

While GAI has potential across all stages of travel, its most direct impact emerges during the trip, where it enables a shift from traditional, human-led tours to dynamic SGE (more in Table 3). To provide a clearer conceptual structure, we propose a taxonomy of GAI applications specifically for the on-site phase of tourism. This taxonomy organizes applications across two dimensions: (1) type of support – the functional role GAI plays in assisting tourists, and (2) level of interaction – the degree of personalization, adaptiveness, and user input required. The goal is to classify emerging functionalities that may supplement, extend, or partially reconfigure information-based aspects of the traditional tour guide role.

The interaction level reflects how responsive and tailored the system is (e.g. itinerary adjustment or cultural interpretation typically involves real-time context sensing and active input from the user, thus rated as high interaction). This taxonomy also supports future research and design strategies by identifying which GAI features are most relevant for each support type. It emphasizes that SGE is not a singular experience, but a set of configurations that emerge through varied combinations of functionalities.

The integration of GAI into self-guided tourism presents a major opportunity across multiple domains, including research, tourist behavior, service provider strategies, and destination management. As these systems evolve, their impact extends beyond convenience and personalization, challenging traditional norms and practices (Hsu et al., 2024). Importantly, the three functionalities should not be viewed as unqualified enhancements. Each carries internal tensions that may complicate their projected effects. By surfacing these trade-offs (between personalization and privacy, responsiveness and overreliance, or context and cultural depth), the framework aims to provide a more balanced understanding of GAI's role in SGE.

The introduction of GAI raises important questions for future research, as these systems redefine how travelers plan (Mladenović et al., 2024a, b), experience (Kim et al., 2023), and reflect on their journeys (Tassiello et al., 2024). Existing studies have highlighted the significance of personalization, real-time assistance, and contextual awareness in enhancing tourist satisfaction (Carvalho and Ivanov, 2024; Kim et al., 2025; Wang et al., 2023; Wong et al., 2024), yet there remains a need to examine more closely how GAI influences traveler behavior across different phases of the travel cycle—pre-trip, on-site, and post-trip. For example, the impact of GAI on real-time decision-making and emotional engagement during travel remains understudied, despite its growing importance as a tool for navigation and itinerary adjustments. Another pressing research area lies in the ethical and cultural dimensions of GAI. As these rely on available datasets to generate recommendations, concerns over cultural bias, accuracy, and the ethical use of personal data become critical. Ensuring that GAI respects cultural norms and promotes responsible tourism requires an investigation into its design and implementation (Sigala et al., 2024). This is particularly relevant in destinations where local customs play a significant role in shaping tourist experiences, and where GAI-generated recommendations may promote inappropriate behavior. GAI's potential role in addressing over-tourism and promoting sustainable tourism also demands scholarly attention. While the ability of GAI to redistribute visitor flows to lesser-known sites appears promising, it is essential to evaluate whether these systems achieve their intended outcomes without creating new challenges (e.g. GAI that redirects tourists from overcrowded landmarks may unintentionally lead to over-visitation of alternative locations).

Finally, the development of metrics to evaluate the effectiveness of GAI is needed. Measures such as user satisfaction, engagement, and alignment with sustainability goals can provide a foundation for optimizing these systems while addressing their potential limitations. Future research must integrate these considerations to provide a clearer understanding of GAI's role in the increasingly changing tourism industry. Future empirical studies may benefit from the development of robust methodological frameworks to examine GAI's influence on tourist decision-making processes. Experimental designs utilizing controlled simulations or app-based interventions in real-world settings could provide evidence on behavioral adaptation, decision quality, and affective responses to algorithmic recommendations. Longitudinal approaches, particularly those capturing post-travel reflections, may further map the growing role of GAI in shaping memory construction and destination attachment. Notably, as the integration of GAI into tourism systems continues to advance, the adoption of innovative, context-sensitive methodologies will be vital to fully capture the complexity and heterogeneity of user experiences.

The convenience of GAI-driven services also comes with trade-offs, including concerns about data privacy, over-reliance on automation, and the potential loss of authentic, human-driven experiences (Dwivedi et al., 2023; Kim et al., 2025; Mladenović et al., 2024a, b; Sigala et al., 2024). To present these insights concisely, we summarize the main benefits and challenges of GAI adoption from the tourist's perspective in Table 4.

Generally, GAI may offer a toolkit for travelers, especially among younger segments who value independence and customization. However, a purely tech-mediated experience risks flattening the social and cultural richness of travel. The challenge lies in designing a GAI that balances efficiency with authenticity and automation. Future research should explore how different user profiles (e.g. digital natives, seniors) perceive these trade-offs, and how GAI may adapt to diverse emotional, cultural, and ethical needs across various contexts.

The adoption of GAI presents both opportunities and challenges for service providers in the tourism industry (tour operators, hospitality businesses, and tour guides). With tourists increasingly using GAI to design and navigate trips (Kshetri et al., 2024), service providers must adapt their offerings to remain competitive. For example, tour operators could integrate GAI to offer hybrid experiences that combine the flexibility of self-guided tours with the expertise of human guides, or hotels could use GAI to provide recommendations, enhancing guest experience. Notably, GAI has the potential to open new revenue streams for service providers, as businesses can monetize their expertise through premium content, such as exclusive cultural insights or tailored activity suggestions. DMOs could license their data to GAI developers, ensuring that local attractions and events are accurately represented in the recommendations.

Still, integrating GAI into tourism services requires careful consideration of the balance between technology and human interaction. While GAI excels at scalability and efficiency (Ivanov and Webster, 2024), human expertise remains irreplaceable in delivering emotional connection and complex storytelling. For example, a local guide's ability to adapt to group dynamics and provide context-rich narratives (Shang et al., 2023) cannot be replicated by GAI. One theoretical implication is that as GAI increasingly delivers convenience and efficiency, service providers may need to rearticulate the unique value of human-led experiences in terms of emotional richness, cultural nuance, and authenticity. To successfully integrate GAI, businesses will need to invest in upskilling, as employees must be equipped to use GAI platforms effectively and interpret GAI-generated insights to improve overall customer service.

For DMOs, GAI offers growing potential to enhance tourism management while promoting sustainable and potentially long-lasting practices. One of the most notable implications is the redistribution of tourist traffic (Papadopoulou et al., 2023). This redistribution reduces environmental pressure and preserves the quality of the visitor experience. In addition to managing visitor flows, GAI can support the preservation of cultural heritage. By embedding local norms and etiquette into recommendations (Mladenović et al., 2024a, b), GAI encourages tourists to engage respectfully with their surroundings. For instance, travelers visiting temples in Thailand could receive reminders about appropriate behavior. Smaller and less-visited destinations also stand to benefit from increased visibility through GAI-driven recommendations. For example, rural areas or underdeveloped regions could attract more tourists by highlighting unique attractions that align with travelers' preferences. This can contribute to more equitable tourism growth by spreading economic benefits to areas that traditionally receive fewer visitors.

However, GAI may also pose challenges for destinations, particularly regarding data dependency and the risk of over-reliance on technology-driven solutions (Grundner and Neuhofer, 2021). If certain locations are disproportionately recommended, even unintentionally, it could lead to new forms of over-tourism. DMOs must collaborate with GAI developers to ensure that recommendations align with capacity limits and sustainability goals. Policymakers must develop regulatory frameworks that ensure transparency in the functioning of GAI algorithms, particularly concerning the origins of data, cultural appropriateness, and environmental implications. Such frameworks might encompass the establishment of certification schemes for ethical GAI deployment within the tourism sector, the formulation of data-sharing protocols between DMOs and technology developers, and the introduction of incentive structures aimed at encouraging GAI to promote equitable distribution of tourist flows. Furthermore, sustained collaboration across governmental bodies, academic institutions, and industry stakeholders will be critical to aligning the development and application of GAI with broader national tourism objectives and international sustainability agendas.

Taken together, the implications outlined above not only illustrate the probable impact of GAI on self-guided tourism but also offer a foundation for future scholarly and policy-oriented exploration. For researchers, the evolving capabilities of GAI invite new empirical investigations into the behavioral, emotional, and cognitive dimensions of tourist interaction with algorithmically driven tools. For policymakers and destination planners, the insights presented point to the urgent need for governance frameworks that align technological innovation with sustainability goals, cultural preservation, and equitable development. As GAI becomes more embedded in journeys, regulatory efforts must ensure data privacy, algorithmic transparency, and inclusivity – especially for marginalized groups and underrepresented destinations. This calls for close collaboration between institutions, policy makers, and local communities to co-create tools that serve both visitor experience and host sustainability. By grounding future empirical research and policy discussions in the realities and evolving dynamics of GAI-enabled travel, the tourism sector can better navigate the opportunities and challenges introduced by this technological shift.

Although this paper examines GAI primarily through the lens of information provision and user experience, tour guiding holds not only an informational function. Guiding is a form of relational and interpretive labor that involves emotional engagement, narrative construction, situational judgment, and cultural mediation – features that remain difficult for GAI to replicate. Framing GAI as a potential substitute, therefore, does not imply a full replacement of the guiding profession, but rather a reconfiguration of its functional boundaries. From a labor perspective, GAI may automate standardized and repetitive aspects of guiding (e.g. factual explanations, translation), which raises concerns about deskilling, platform intermediation, and shifting value capture toward technology providers. At the same time, these changes may open space for reskilling and hybrid models, where human guides focus on experiential, ethical, and culturally embedded dimensions of tourism while delegating informational tasks to AI-supported systems. These transformations are embedded in broader issues of governance, regulation, and local livelihoods. Tour guiding is often licensed, culturally protected, or institutionally regulated, and the introduction of GAI-mediated alternatives may disrupt existing professional structures. While a full political-economic analysis lies beyond the scope of this paper, we emphasize that any discussion of “replacement” must be understood as partial, conditional, and context-dependent rather than absolute.

This paper conceptualized how GAI may shape self-guided tourism experiences through personalization, real-time assistance, and contextual awareness. Rather than presenting GAI as a full alternative to traditional tour guiding, the paper argues that GAI is better understood as a technology that may complement, extend, or selectively reconfigure information-based and logistical aspects of guiding under specific conditions. Its value lies primarily in supporting autonomy, flexibility, accessibility, and responsiveness in independent travel. However, its limitations remain evident in areas such as emotional engagement, cultural mediation, situated interpretation, and ethical accountability.

While the framework draws on known GAI features, it offers a novel, structured lens to examine their combined influence on SGE. It is not intended as a conceptual replacement of tour guiding, but rather as a tool to guide empirical work and design interventions. However, it is important to acknowledge that this paper is conceptual and does not present original empirical data. In particular, Table 2 presents hypothetical configurations of GAI features in self-guided tourism settings, which require empirical validation in real-world use cases. While the proposed framework is grounded in a review of current literature and technological trends, its implications remain theoretical. The hypothetical scenarios and expected outcomes described throughout the manuscript aim to spark further investigation but require validation through empirical research, including user testing, real-world case studies, and longitudinal designs. Another key limitation lies in the assumption that GAI systems will be adopted and used uniformly across different tourist segments and destination types. In reality, factors such as digital literacy, device accessibility, cultural attitudes toward AI, and infrastructural readiness vary significantly and may influence the impact and feasibility of GAI-supported self-guided experiences. Moreover, while the paper emphasizes the positive potential of GAI (e.g. personalization, accessibility, and sustainability), it is important to recognize that these outcomes are not guaranteed. Several unintended consequences and ethical dilemmas must be considered. These include the risk of algorithmic bias, privacy and surveillance concerns, and the reinforcement of popularity-based feedback loops (e.g. the “lemming effect”) that could lead to overcrowding at already popular sites. There is also a risk of cultural homogenization, where GAI-generated content flattens local diversity in favor of widely appealing but less authentic experiences. Additionally, over-reliance on GAI may reduce exploration and decrease opportunities for human interaction – two very important aspects of independent travel (Ren et al., 2024). Future research should empirically test both the positive and negative outcomes of GAI in tourism settings and explore how human-AI interaction, regulatory frameworks, and design ethics can shape more inclusive, context-sensitive, and value-aligned applications of this technology. While the present framework focuses on user-facing functionalities of GAI in shaping SGE, we emphasize that broader labor, governance, and equity implications remain underexplored. Future studies should examine how GAI adoption affects tour guide livelihoods, value capture, and destination-level policy frameworks.

This framework offers a structured but provisional lens on how GAI may shape self-guided tourism experiences. It surfaces not only affordances but also conceptual tensions that require further theorization and empirical validation across diverse contexts.