In today's tech-savvy society, every encounter, from mobile clicks to augmented reality try-ons, affects preferences and decision-making, changing how people act (Schiffman & Wisenblit, 2019). For example, in 2024, 51% of smartphone users used AR to shop, showing how quickly immersive technologies are being used in business (Threekit, 2023). The move from physical storefronts to static websites and now to dynamic, responsive interfaces like in-app notifications and QR-enabled dressing rooms has completely changed how people shop in real time. Also, the use of artificial intelligence has turned this progression into a revolutionary project that changes not only how consumers act but also the mental processes that lead to purchases. AI-driven solutions improve decision-making, increase engagement, and create value at every digital touchpoint by allowing for adaptive personalisation and predictive assistance.

 The metaverse takes this change even farther by giving us fully realised digital environments. The metaverse market was worth $105.40 billion in 2024 and is expected to be worth $936.57 billion by 2030, with a compound annual growth rate (CAGR) of 46.4% (Research and Markets, 2025). The metaverse made $3,921.4 million in India in 2024 and is predicted to grow to $43,732.4 million by 2030, at a CAGR of 49.5% (Grand View Research, 2025).

AI-driven personalisation adds to these immersive worlds by capturing and interpreting real-time behavioural clues like gaze direction, voice tone, and avatar movements to create experiences on the fly. For example, a virtual bookstore may move its shelves around in the middle of a conversation to show what the user is interested in, and a workout arena in Spatial.io could change the lighting and music to match the user's energy level. All of these dynamics come together to change what participation means, turning consuming into a smooth, co-creative journey that changes value, behaviour, and identity in digital marketplaces all the time.

The metaverse is a persistent, shared, and immersive three-dimensional (3D) virtual environment that seamlessly combines digitally enhanced physical reality with physically persistent virtual spaces. It lets users and digital objects interact with each other in real time across multiple platforms (Lee et al., 2021; Milgram & Kishino, 1994). It includes a wide range of experiences, from augmented reality (AR) overlays on the real world to fully virtual worlds. These are made possible by enhanced networking, rendering, and interface technologies (Abdelmagid et al., 2025).

The metaverse is forming in the changing digital world by changing how people engage with brands and how they buy things. AI personalisation is one of the things that makes real-time fit to experience change how people act. AI employs things like gaze direction, intonation, and avatar movements to create personalised experiences that keep users interested and affect their buying decisions. A virtual store would display users product suggestions and lead them through their buying experience in a more customised way based on how they interact with the store. Such interactions build stronger relationships with customers that eventually lead to brand loyalty and conversion behaviour. The metaverse is a permanent, immersive 3D virtual world that gives marketers a whole new approach to think about how to connect with customers. As AI personalisation algorithms change to fit people's shifting tastes, the metaverse shapes that connection. So, the metaverse quickly takes on the role of guiding consumer participation and decision-making in the digital marketplaces.

Artificial intelligence-powered personalization uses algorithms, machine learning, and behavioral analytics to understand and predict what users might want. This allows virtual spaces to progressively change and adapt to users by providing suggestions, customizing avatars, or delivering content that boosts user engagement and satisfaction. Personalization is AI-driven through platforms like Netflix, which bases its show recommendations on viewing history, whereas Amazon recommends products on the basis of past purchases. In the fashion industry, Zara uses AI to forecast trends or customize the shopping experience. Tools that influence consumer behavior are basically recommending content that "feels" like real personal interaction, thereby increasing engagement and loyalty through content that is more relevant and timelier. As users navigate the metaverse, every movement and interaction is analyzed to tailor their shopping experience for virtual goods to engaging acts of socializing (Alawadh and Barnawi, 2025). AI-level personalization is generating the new domain in consumer behavior and engagement where both virtual and physical interactions simultaneously mold experiences. In a more virtual space, the AI offers tailored experiences based on digital behavior, while physical interactions in preference-building, such as going to the store or social cues in the real world, intermingle to enrich consumer experience.

But in spite of monumental strides in AI personalization techniques, metaverses do face big challenges: ones that range from the complexity of implementing the technology itself, to the serious issues of data privacy, algorithmic bias, and the ethics of personalized user experience. As this ecosystem matures, it becomes very important to understand the implication of AI-driven personalization on consumer behavior, user engagement, and the actual virtual experience. Whereas AI and metaverse share increasing fluxes of intersectionality in research, very limited work exists exploring the actual intersection of AI-based personalization and consumer behavior in the metaverse (Bibri and Jagatheesaperumal, 2023). This situation, or rather the absence of a holistic understanding thereof, limits the ability of businesses to design experiences that meaningfully cater to the needs of the users, while simultaneously avoiding certain risks pertaining to exploitation of data and algorithmic manipulation.

Aim and objectives and questions

Aim of the Study:

The aim of this study is to explore the influence of AI-powered personalization within the metaverse ecosystem, specifically focusing on its impact on user experience, behavior, and engagement.

Objectives of the Study

• To explore how AI-driven personalization affects user experience, engagement, and behavior
• To identify key AI technologies used in the metaverse for personalization
• To assess the challenges and ethical considerations associated with AI personalization.

Research Questions

• How does AI-driven personalization impact user experience and behavior in the metaverse?
• What are the key AI technologies enabling personalization in the metaverse?
• What are the potential risks and challenges associated with AI-driven personalization?

Impact of AI-Driven Personalization on User Experience & Behavior

A few AI personalization solutions help carbonize webpage views and bring in user changes within the metaverse, namely immersion, engagement, and decision-making.Evolution of telepresence and flow states allow developers to induce the feeling of actually being "there," which then allows users to engage more deeply with various digital spaces. Bibri and Jagatheesaperumal (2023) assert that, in the metaverse, real-time interactions occur as adaptive environments respond to user actions to deepen engagement and increase the feeling of presence. This level of personalization is accomplished by AI algorithms, hence creating an environment that changes as undertakings change with time per individual need to maximize user satisfaction and interaction (Almeman et al., 2025).

Engagement and motivation factors allow AI systems to garner attention through real-time data such as movements and observable behavior; by doing it so, it renders virtual experiences more relevant and engaging. Through AI, gamification and rewards stimulate the further motivation of the user with incentives while interacting and increasing levels of engagement (Mourtzis et al., 2022). These also encourage stronger engagement by rewarding users for performing actions, thus leading to more dynamic and participatory experiences. AI also builds a space for emotional and affective connections through mood-aware soundscapes and haptic feedback that react to changes in the user's emotional state, increasing user immersion and emotional involvement. Emotional contagion in social VR spaces allows for the sharing of moods and emotions, which then forms even stronger social connections and engagement. This emotional interaction serves as a great enhancement for user experiences, also heavily impacting decision making and purchasing behavior as users increasingly feel interlinked and influenced by the ambience and social dynamics of the metaverse.

Besides that, the AI contributes to decision-influenced affective responses dealing with impulsive versus deliberative buying. By preventing choice overload and aiding discovery, AI infrastructures foster more efficient navigation and actualization of purchase journeys within virtual shopping environments (Rane et al., 2023). Finally, the AI impacts social dynamics and community, mainly through social proof moderation and enabling co-shopping experiences. Trust is created through virtual agents, AI shop assistants, who provide personalized support and where the alleged comfort is nonexistent in a virtual environment (Kanade & Batule, 2024).

Technological Foundations of AI and Personalization in the Metaverse

Metaverse AI personalization is built on tools that study the clients' patterns of behavior, their interactions, and their preferences. One of the key technologies is ML, where AI systems learn or adapt to data inputs. Alnaser et al. (2024) explain ML is essential for personalizing virtual environments by assessing the likely preferences of users and suggesting pertinent content. Predictive algorithms exploit real-time personalizations that evolve the unfolding of the user experience as new interactions take place (Soliman et al., 2024). The continuous learning enables AI systems to improve with time, which leads to a better user experience.

Metaverse-level personalization includes technologies such as digital twins that create digital representations of real-world entities to anticipate user behavior and interactions. Almeman et al. (2025) say that these digital twins let the virtual world change in real time based on what each person wants and needs, making sure that everyone has a unique experience. Wei et al. (2023) say that AI-powered recommendation engines are also used to suggest products, experiences, or information to specific users based on their prior behaviour or declared preferences. These technologies would therefore make the metaverse an ideal, curated place to be, which would make people more engaged and happier because they would be having meaningful interactions on a personal level. Also, virtual assistants and smart avatars are quite important for making the metaverse seem like home. Customised avatars would guide users around the virtual space, reacting to what they do and anticipating their needs for smooth interaction. Bibri and Jagatheesaperumal (2023) say that avatars with AI make interactions smoother and more fluid by changing in real time. Mourtzis et al. (2022) say that platforms that are based on people's preferences and behaviours promote value creation, which leads to more consumer engagement and satisfaction. This kind of AI personalisation changes how people buy things by making it easier for them to make decisions and building emotional ties, which leads to more loyal customers and more virtual product sales.

AI technology might also make NLP and CV more personalised. NLP systems can understand and process spoken commands, which makes communication in virtual environments feel more natural. Ibrahim (2024) says that these kinds of technologies bridge the gap in communication between consumers and AI, making sure that the experience is unique and doesn't seem robotic. However, with the mighty potential of such advancements come also the risks of a colossal complexity in AI systems in the metaverse. Talk of algorithmic bias, transparency, and privacy are more often than not front-liners during any discussion on AI personalization within virtual environments. Kanade and Batule (2024) argue fairness and transparency are to be maintained through AI-driven systems not only to stimulate the metaverse for popular adoption but also to build the trust of the users.

Key AI Technologies Enabling Personalization in the Metaverse

Metaverse-type AI personalizations operate through a few key technologies that enhance the user experiences by adapting the virtual environment to individual preferences, behaviors, and interaction models. Recommender systems, by means of collaborative and content-based filtering, play a major role in personalizing content in 3D virtual environments. Collaborative filtering looks at what a certain user behaviour-based kind of recommendation is most suitable for an individual user; content-based filtering suggests items close to what the user has already been interacting with. Predictive analytics leverages AI mechanisms to analyze past behaviors to detect future preferences, making metaverse experiences continuous and personal (Alnaser et al., 2024). Reinforcement learning agents optimize the user experience by adapting to the evolving nature of user interactions in real-time, thereby producing a dynamic environment that evolves with each engagement (Soliman et al., 2024).

Computer vision and gaze tracking empower end users by interpreting visual attention. Eye-tracking-based mechanisms detect user interest and drive real-time changes on the user interface in such a way that the content is displayed in the most engaging fashion (Almeman et al., 2025). Meanwhile, body-pose estimation further augments the immersive experience by allowing AI systems to understand user intent and attention, which could then be used for personalized interactions or suggestions of virtual products (Wei et al., 2023).

NLP allows for voice and text input recognition and processing by AI, thus breaking down communication barriers between the user and virtual environments. Conversational agents or companions guide the user through the virtual environment, rendering personalized assistance and support. By combining emotion-aware dialogue, these agents become more engaging interactively, nurturing empathic discussions with users that run deeper in emotional significance (Mourtzis et al., 2022).

In a dynamic profiling style, AI systems adapt to user preferences and personalities to provide continuous personalization, irrespective of the platform. Cross-session learning observes user preferences and transfers these preferences between sessions to enable an effortless user experience. In addition, multimodal signal fusion, which integrates clickstream, gaze, gesture, and biometric data for instantaneous processing, serves the purpose of deeper personalization. However, such processing is curtailed because of edge computing limitations (Ibrahim, 2024). With these AI-enabled personalization features, the metaverse is able to broaden consumer experiences that directly affect purchase intent and consumer decision-making. These technologies ensure there is enhanced engagement and personalization that leads users toward making better-informed decisions and cause them to be more satisfied with virtual environments.

Potential Risks & Challenges of AI-Driven Personalization in the Metaverse

With AI-driven personalization in the metaverse, very high possibilities are envisaged, but challenges are ever-present. One of the foremost concerns is data privacy. AI systems mandate the incessant collection and analysis of actual user data based on behavior, interaction, and preferences. It drills down to the collection, storage, and sharing-use questions of such data. Keeping in view that users' personal information must stay protected by data protection means and the transparent use of such data, Almeman et al. (2025) advocate. The real challenge regarding personalization and privacy concerns becomes more apparent in the metaverse, where users generate prodigious amounts of personal data across many platforms. Another issue stands to be algorithmic bias. AI intends to build systems for personalization, however, the current data operating in systems is greater than society and inseparably intertwined in social biases that translate unfair or distorted personalized experiences. Soliman et al. (2024) think that fairness in AI-driven personalization entails the design of nonbiased algorithms and continuous monitoring of AI systems for their discriminatory impacts. This kind of unfairness might translate into personalized experiences segregating some groups of people or even perpetuating offensive stereotypes. In the future, AI could be interfaced with emerging blockchain technology to alleviate some aspects of these issues. Being decentralized, blockchain can offer solutions to privacy and transparency issues, empowering the user to have some say regarding what happens to his personal information. According to Kuru and Kuru (2024), blockchain technology is a way that users can verify and authenticate their data whilst protecting their privacy, fighting data breaches, and unauthorized access via an encryption mode that protects personal user information, thereby also encouraging trust in AI-backed systems.

Another layer in the future of AI-powered personalization within the metaverse lies in enhancing the AI algorithms and machine learning models. Kanade and Batule (2024) point out that newer AI models, which will be more accurate and efficient, will be able to deliver even more sophisticated personalization, which means richer and more engaging experiences, for the users. Machine learning algorithms, which include reinforcement learning, can train AI systems to dynamically respond to user behaviors and generate content that is more pertinent. As algorithms mature, the greater city's personalization experience will operate in just the most suitable way for users, whose experiences will be enticing and demand response abilities.

AI integration with IoT and digital twin simulations also provides some exciting prospects for metaverse personalization. In this regard, Alnaser et al. (2024) propose that IoT setups can generate real-time data from the physical world, feeding it into neighboring virtual spaces for streamlining user experiences. Digital twins, virtual counterparts of physical entities, are used to simulate context and preferences of users for hyper-personalized experiences in real-time. A marriage of AI, IoT, and digital twins can give a form of personalization that cues on changes in both virtual and real-world entities, thereby putting to simultaneous metaverse experience.

Literature Gap

The gap is addressed by the study, as it presents a major review of the relevant literature relating to AI-powered personalization in the metaverse, looking at how AI could be applied to enhance consumer experience while considering ethical concerns around such technology. Hence, the study will attempt to address the technological and behavioral gaps with ways in which companies could better shape behavior in virtual environments. Furthermore, it will provide some interesting research avenues and shed light on additional on-the-side concerns requiring further attention, especially the ethical concerns associated with AI-driven personalization.

Research Philosophy

In this study, the constructivist research philosophy is embraced while emphasizing the analysis of subjective experience and interpretation of individuals in the natural environment. Constructivism looks distinctly relevant for studying AI-driven personalization in the metaverse, as it recognizes the complexities of social phenomena-in this case, the socially constructed reality.

Research Design

This research uses a qualitative design that allows for describing the lived experiences and interactions of metaverse users. This design is ideally suited to tackling such complex issues as user engagement and behaviour, especially when quantitative methods cannot suffice and come short in these virtual environments. By adopting the qualitative perspective, this study attempts to dig up more patterns and themes tied to AI personalization to offer a more profound insight into its influence on the user journey without placing generalizations upon the findings.

Data Collection

The secondary research will be qualitative, looking at case studies, reports, and other material on AI personalisation and the metaverse. This approach creates a big pool of knowledge that may be used to spot trends and critically look at past research. To keep the study up to date on changes in AI and the metaverse since 2017, data will be collected from peer-reviewed papers, books, and industry reports.

Table 1. Peer reviewed studies related to Metaverse AI

Data Analysis

We use thematic analysis to look for patterns that happen over and over again in the secondary data. This method creates basic programmes, finds themes that come up again and again, and then refines them to get useful information. The focus of this examination will be on how AI-based personalisation changes how users act and feel in the metaverse.

Search Strategy

Using databases like Google Scholar, Scopus, and Web of Science, the search method will look for secondary sources that were published after 2017. The search will be guided by words like "AI-powered personalisation," "metaverse," and "user engagement."

Inclusion and Exclusion Criteria

Inclusion Criteria:

• Publication Date: Only papers published between 2017 and 2025 were included to ensure relevance and reflect recent advancements in AI, metaverse, and consumer behavior.
• Source Type: Peer-reviewed journals, conference papers, and academic books from reputable sources (e.g., Scopus, Web of Science (WoS)).
• Relevance: Papers focusing on AI technologies (machine learning, predictive algorithms, NLP), the metaverse, and consumer behavior (engagement, decision-making, purchase behavior).
• Methodology: Studies employing qualitative, quantitative, or mixed methodologies that provide a comprehensive understanding of consumer behavior in AI-driven environments.
• Geographical Focus: Papers that address global or cross-cultural implications in AI personalization within the metaverse.

Exclusion Criteria:

Publication Date: Papers published before 2017 were excluded due to the focus on recent developments.

Non-Peer-Reviewed Sources: Papers from non-peer-reviewed journals or sources were excluded to maintain academic rigor.

Irrelevant Focus: Papers primarily focused on technical/engineering aspects of AI or the metaverse without addressing marketing implications or consumer behavior.

Lack of Consumer Behavior Analysis: Studies that did not examine or discuss consumer behavior, engagement, or purchase decisions in the context of AI personalization within the metaverse.

Methodological Limitations: Papers lacking clear or detailed methodologies, especially those not applicable to consumer behavior research in the digital space.

Ethical Considerations

Ethical issues include giving credit where credit is due and respecting other people's ideas.  There are fewer worries about privacy and permission because no primary data is acquired. This makes sure that research is done in a clear and moral way.

Effects of AI-Driven Personalization on User Experience, Engagement, and Behaviour

Enhanced Immersion and Presence

AI-based customisation makes the metaverse more immersive and present, which affects how people act when they are engaged and decide to buy anything. Telepresence is at the heart of user engagement. It is the feeling of "being there" in a virtual environment where AI-driven systems act and respond to input right away, making the virtual area feel real. These AI technologies make experiences more personal by taking into account how a user acts and what they like. This strengthens emotional ties and keeps users more engaged. So, the AI changes content, interactions, and product recommendations in real time based on each user's wants and historical behaviour, making each user's journey unique but still logically consistent. These personalised experiences add to the satisfaction of spending time in the metaverse, which can lead to purchases when buying becomes more intuitive and emotive (Almeman et al., 2025). The metaverse is a very responsive place that marketers can use to change their services in real time to get more sales and keep customers loyal. This is because it is always changing thanks to AI (Rane et al., 2023). This continuous adaptation to user preference continues to ramp up user engagement while heavily influencing purchase decisions, thus placing AI at the heart of consumer-behavior movement in virtual marketplaces.

Engagement Patterns and Purchase Decisions

Personalized experiences, later propelled by AI, define engagement patterns and the decision-making process. Based on user data, AI systems propose contents, products, and experiences deemed to be in line with user's own behaviors and preferences so that the dealings may even be more interesting to the user. This raises the level of engagement of any user in the virtual environment, making the engagements more intensive regarding length and more profound concerning intensity. It guides users from impulsive to more considered forms of purchasing. It also eases the decision with personal suggestions and makes the user feel confident about the decision and fulfilled while shopping from the store. This conversion of impulsive to considered buying is the better-hand work of AI, which refines the virtual commerce platform by assisting in making an informed decision that uplifts the user's satisfaction level alongside engagement (Almeman et al., 2025).

Behavioural Shifts and Community Dynamics

AI also preserves social dynamics in the metaverse between users and fosters their interaction. By AI-assisted social proof and co-shopping experiences, it helps users trust each other and collaborate within virtual communities. Personalization impacts community dynamics toward fine-tuning social interactions to users' interests and behaviors toward the realization of a more connected and loyal community. AI recommendations for social activities or group experiences in accord with previous behaviors motivate users to interact more within virtual communities, thus building a sense of belonging and trust, which promotes engagement and active participation in the long term. The effects of personalization extend beyond the individual to the development of a shared ecosystem of collaborative and productive metaverse communities (Kanade & Batule, 2024).

Key AI Technologies Enabling Personalization in the Metaverse

Personalization based on AI improves user engagement and decision-making within the Metaverse and enhances immersion and presence. Telepresence comprises the feeling of "being there" inside a virtual space, whereby an AI acts on inputs fed into it in real time to render the virtual environment more real and relevant. By simultaneously reinforcing the emotional connection on an individual level and augmenting consumer engagement by providing interactions that feel real on their faith and that peoples' behaviors and preferences) AI keeps constantly changing the content, product recommendations, and interactions based on the past behavior of the user, thus creating a smooth experience, which fosters a sense of utility and satisfaction in users, becoming a fair amount toward decision-making (Almeman et al., 2025). The evolving nature of AI allows for engaging modification of virtual experiences for its users via different aspects such as personalized recommendations, customization of environments, and dynamic alterations in real-time, thus keeping the user glued in the Metaverse for a longer time, hence impacting their purchasing propensity. AI personalizes and adapts everything, thereby cultivating gratification, further cementing the loyalties, and culminates into repeat purchases. By increasing engagement and emotion, AI guides the consumer through the buying proposition, setting brands as trusted partners in the Metaverse (Rane et al., 2023). This migration describes how consumer behavior is being transformed and purchase decisions are being influenced by AI personalization in these immersive virtual spaces.

Challenges and Ethical Considerations of AI-Driven Personalization

Privacy, Data Security, and Consent

Personalized AI experiences in the metaverse had jarring implications for privacy, security, and consent. AI systems require storing huge bases of personal data to fine-tune individual experiences, which brings in matters with how data is collected, stored, and utilized. Users barely hold any control over their data, and regulations should be established as a tool along with explicit mechanisms of consent. Users must know exactly what will be done with their data to protect it and give their consent to the management of that data (Almeman et al., 2025). Without such safeguards, the trust in systems assisted by AI would be broken, and users will always be reluctant to partake in the virtual happenings.

Psychological and Ethical Implications

Excessive personalization can exert negative psychological effects-one being toward the gradual erosion of users' autonomy. While AI systems personalize interactions based on an individual's preferences, users tend to become quite dependent on options suggested by AI, thereby weakening their abilities for independent decision-making (Narasimhan, 2024). Another issue is algorithmic bias since AI systems unintentionally strengthen certain stereotypes or marginalize specific groups into receiving an inequitable virtual experience. Hence, in the creation of fair and equitable virtual environments, it is necessary for AI systems to take hold of inclusiveness and shall not become a tool to perpetuate pre-existing biases (Soliman et al., 2024). One other dangerous phenomenon resulting from over-reliance on AI may be the creation of filter bubbles, wherein the user only gets exposed to information based on his present preference, limiting his exposure to all new thoughts and experiences (Kanade & Batule, 2024). Addressing such psychological and ethical challenges remains germane to retaining trust and equity in AI-enabled systems.

The study portrays AI-driven personalization as a drastic change in the metaverse, affecting the spectrum of user experience, engagement, and behavior. The existing AI technologies, such as machine learning, predictive algorithms, and digital twins, allow dynamic interaction with users, giving the user a personalized experience, although aspects such as data privacy, algorithmic biases, and transparency pose serious challenges. Any metaverse allowing sustainable growth and inclusivity must resolve these ethical and technological concerns to enable personalized, secure, fair, and diversified experiences that gather user trust and glorify user autonomy.

Recommendations

• Enhance Data Privacy: Implement robust data protection measures, ensure transparency in data handling, and explore blockchain for added security.
• Address Algorithmic Bias: Regularly audit AI systems to use diverse training data, ensuring fairness and equity in personalized experiences.
• Promote Transparency: Ensure users understand AI algorithms and have control over personalization settings to build trust.
• Encourage Content Diversity: Avoid filter bubbles by exposing users to a wide range of content, promoting exploration within virtual spaces.
• Establish Ethical Governance: Develop ethical frameworks and collaborate with policymakers to ensure responsible AI use in the metaverse.

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