In our systematic review, a limited number of studies (6 papers, 20%) investigated the role of user characteristics in interactions between users and ECAs (see Table 3). Notably, gender of the users emerged as a key sociodemographic factor (4 papers, 13%), indicating that females generally perceive ECAs as more attractive than males. Female users generally exhibited higher levels of empathy and reported less tension and annoyance toward the ECA (Belda-Medina and Calvo-Ferrer, 2022; Lahav et al., 2020; Lisetti et al., 2004; Sajjadi et al., 2019). Age also played a significant role, with younger participants finding ECAs more attractive (Lisetti et al., 2004; Zhang et al., 2024). Another noteworthy factor is the flow state, which appears when users become deeply immersed and fully engaged in an interaction with the ECA, experiencing a high level of focus and reduced awareness of time or external distractions. Specifically, the flow state of the users positively predicted the perceived anthropomorphism of the ECA in one study rated with a moderate overall methodological quality (Saad and Choura, 2022). Despite similar access to technology, Polish users reported more positive attitudes and a greater perceived ease of use toward ECAs for learning compared to Spanish users, suggesting that ease of use may be linked to overall user attitudes. One possible explanation for the less positive attitudes among Spanish participants is their broader familiarity with advanced conversational agents like Alexa, Siri, Cortana, Google Assistant, and Watson. This familiarity may lead to higher expectations and quicker disappointment due to the habituation effect, reducing curiosity and novelty during interactions with simpler ECAs (Belda-Medina and Calvo-Ferrer, 2022). Interestingly, students from the Faculty of Social Sciences perceived the ECA delivering career counseling as more attractive and were more likely to recall its recommendations, compared to students from the Faculty of Exact Sciences. One possible explanation given by authors is that students in Exact Sciences may have less time or interest in engaging with such activities outside their core studies (Lahav et al., 2020). A research investigation focused on how user personality traits, as characterized by the Big Five Model, affected perceived attractiveness (Lisetti et al., 2004). Participants exhibiting higher levels of openness to experience tended to find the ECA more attractive (Lisetti et al., 2004). However, the study was rated with a weak overall methodological quality.

In our review, we observed that ECAs studied were female, half-body, and dynamic in approximately one-third of the cases. Full-body ECAs generally elicited higher levels of anthropomorphism but were also more prone to triggering the uncanniness feelings, especially when their motion was dynamic (Buttussi and Chittaro, 2019). Conversely, half-body and face-only ECAs, while less anthropomorphic, received lower uncanniness ratings (Conrad et al., 2015). The most studies included in the systematic review (18 papers, 62%) examined how various features of the ECA influence user perceptions, such as physical features, facial expressions, communication style, and personality factors (Table 3). Among these features, the facial expressions (6 papers, 20%), and communication style of the ECA were the most extensively explored (6 papers, 20%), but the findings were mixed. A customizable ECA proved to be more attractive, offering users the flexibility to change its gender, race, and name (Belda-Medina and Calvo-Ferrer, 2022). The customisation feature seems to be more important to female users. Overall, users of both genders showed a preference for a female ECA, though choices also included male and non-binary ECAs. Interestingly, an ECA that resembled the user ethnically did not necessarily enhance attractiveness. Furthermore, one study with a good methodological quality found that an ECA with a celebrity appearance in a highly anthropomorphic condition was perceived as less uncanny than the same celebrity represented with a cartoonish appearance (Song and Shin, 2022). When familiar faces are presented in low-anthropomorphism styles, they may trigger stronger feelings of uncanniness, likely because users expect a more realistic physical features when the ECA is based on a real person.

While some research indicated that ECAs with a range of facial expressions were considered more attractive than those without any expressions, the results were not uniform. Eye gaze alone cannot induce uncanniness (Zheleva et al., 2023), probably because more facial features are required in order to induce uncanniness. ECAs with facial expressions were rated higher in terms of perceived compassion and intelligence, and they seemed to encourage more interactions initiated by users (Luo et al., 2023; Min et al., 2024). Moreover, participants spent more time interacting with the ECA displaying emotionally adaptable facial expressions (Wang et al., 2021). However, inconsistencies arose within the same studies. For instance, users did not consistently rate the ECA with facial expressions as more attractive (Conrad et al., 2015; Hale and Hamilton, 2016). Furthermore, the presence of facial expressions in the ECA did not necessarily lead to perceptions of increased friendliness when compared to an ECA without facial expressions (Creed and Beale, 2012). Interestingly a slightly higher number of studies concentrated on positive emotions such as happiness and joy, but some studies examined the effect of negative emotions such as disgust. Users generally perceived positive facial expressions as being more friendly and trustworthy. In contrast, negative expressions were generally associated with lower trust, with users often describing such ECAs as unfriendly or unapproachable (Luo et al., 2023). However, around 60% of users said ECAs with positive expressions looked the friendliest, whereas only 3% did so for those displaying disgust. Interestingly, a small number of users perceived the disgusted ECA as more professional (Luo et al., 2023). Fearful expressions were found to increase fear among users, especially those who had received prior safety training (Buttussi and Chittaro, 2019). These findings suggest that emotional expressions influence user perceptions and emotional responses, but their impact may depend on context, user expectations, and task relevance, with no universally optimal emotional strategy.

In addition to non-verbal cues, the systematic review also explored the role of verbal communication in ECAs. The findings suggest that ECAs with enhanced verbal communication skills are often perceived as more user-like and attractive. Specifically, an ECA expressing joyful messages is regarded as more attractive and helpful compared to an ECA neutral messages (Buttussi and Chittaro, 2019; Ham et al., 2024; Hao et al., 2024). ECAs that used personal greetings like ‘Hello,’ and “Have a nice day,” were rated as more attractive than those with a more straightforward communication style (van Pinxteren et al., 2023). Notably, ECAs that conveyed happiness through captions and emojis were perceived as having greater emotional intelligence than those expressing other emotions like lust or sadness (Ham et al., 2024). Joyful messages were characterized by the use of more words, positive affect terms, and expressive punctuation, like exclamation marks (e.g., “I am happy to help!”). Humor such as amusing stories can enhance the perceived attractiveness of an ECA (Hao et al., 2024). Furthermore, an ECA that engages in friendly communication, evidenced by initiating conversations and employing phrases such as “I’m sorry,” was typically perceived as more attractive. This perception remains consistent regardless of the ECA being represented as male, which was a less-used gender representation in the present review (Prendinger and Ishizuka, 2001). Beyond the quality of information received from ECAs, socio-emotional capabilities were also valued. The ECA could recognize and reflect the user’s emotional state with messages like “It seems you are facing some challenges” and put these feelings into a broader context by saying: “Many people may encounter these difficulties” (Schouten et al., 2017). Additionally, the ECA’s social skill of not interrupting the user during a conversation can make users initiate more interactions (Schouten et al., 2017). Expressions of verbal encouragement, blending affirmation and motivational feedback, such as “Keep going, you are doing well!” can help alleviate user stress (Neumann et al., 2023). This effect supports the extension of the Buffering Stress Theory, traditionally applied to interpersonal relationships, to interactions between humans and ECAs.

Few ECAs were not limited to scripted interactions but could actively recognize and adapt to the user’s emotional states, analyzed through valence-arousal mapping of emotions (Prendinger et al., 2006). Emotional states of the user were detected based on physiological data, including skin conductance (i.e., measured via electrodes on the index and small fingers of the dominant hand of the user) and facial electromyography (i.e., with sensors placed on the use’s left cheek). This input allowed the ECA to classify emotional states based on valence (positive–negative) and arousal (low or high). For example, the emotion “relaxed” has a positive valence and a low arousal (Lang, 1995). Another similar framework, PAD, classifies the emotional states based on three dimensions: Pleasure (vs. displeasure), Arousal (vs. sleepiness), and Dominance (vs. submissiveness) (Becker-Asano, 2008; Kshirsagar, 2002; Russell and Mehrabian, 1977). This framework allowed the ECA to express 18 emotional states including: hopeful, peaceful, bored, annoyed, neutral, depressed, sad, happy, surprised, anxious, angry, overwhelmed, afraid (Sajjadi et al., 2019). These emotions were used to simulate personality traits based on Big Five model (Digman, 1997). For instance, the extraverted ECA expressed emotions that were high in dominance. Such an ECA was sociable, assertive, and maintained direct eye contact for 90% of the interaction time with the user (Sajjadi et al., 2019). In another study, an extroverted ECA, which initiated communication and showed positive emotions such as gratitude, was perceived by the users as attractive (Hao et al., 2024). However, an extroverted ECA can also show anger, manifested through mild frown eyebrows, direct eye contact, shoulders up and sideway posture (Sajjadi et al., 2019). In contrast, an introverted ECA was characterized by emotions low in dominance. Such an ECA was more submissive, showed lower assertiveness, and expressed more negative valanced emotions such as sad, overwhelmed and afraid, with the latter conveyed through slightly raised eyebrows, avoided eye contact, dropped shoulders, and a hand placed on legs (Sajjadi et al., 2019). An introverted ECA maintained eye gaze only 30% of the interaction time with the user, compared to 60% during a neutral emotional state, and it was perceived as more unsettling. While extraversion has received more attention in ECA design, other personality traits have been less frequently explored. For example, only one study focused on the agreeableness personality factor, where an ECA perceived as helpful and forgiving was also considered more attractive by users (Prendinger and Ishizuka, 2001). However, traits such as neuroticism, openness to experience, and conscientiousness were largely neglected in user-ECAs interactions.

Furthermore, most of the included studies have focused on the perceived attractiveness of the ECA, probably because this dimension closely mirrors patterns observed in human social interactions. In contrast, the experience of uncanniness is less well understood, particularly because we lack well-established theories of the UVE in interpersonal contexts. As a result, researchers are still working to interpret and reconcile the often inconsistent findings related to this phenomenon. In the reviewed studies, the ECA perceived as most uncanny was also rated highest in anthropomorphism, specifically in terms of both physical and mental features. One plausible explanation is that users tend to expect ECAs to behave in a mechanical, task-oriented manner. When an ECA displays a high degree of autonomy, such as planning, expressing emotions, or demonstrating independent reasoning, this may conflict with users’ expectations and elicit discomfort (Yin et al., 2021). To counteract this effect, some researchers propose designing ECAs to appear more dependent on human guidance and less capable of fully autonomous behavior. However, the relationship between anthropomorphism and user perception is not straightforward. While greater anthropomorphism may increase the risk of uncanniness, it simultaneously raises users’ emotional expectations. For instance, ECAs with highly human-like appearances are often expected to be more emotionally attuned and responsive (Zhang et al., 2024). When such ECAs successfully express empathy or concern, they tend to be perceived as more attractive. This alignment between anthropomorphic appearance and high emotional features can reduce feelings of uncanniness. Conversely, when emotionally expressive expectations are unmet, users may react more critically, especially toward ECAs that appear highly human.

In Figure 2, we present a synthesized overview of the evidence related to factors associated with the UVE outcomes in the studies reviewed. The figure is partially data-driven, based on the findings from the included studies that examined either ECA features or user characteristics. The summary figure draws inspiration from previous work (Loveys et al., 2020a). Our analysis revealed significant relationships between several user characteristics, such as gender and age, and perceived attractiveness of the ECA. We found a significant association between UVE outcomes and various ECA features, including non-verbal features, customization options, humor, friendliness, ECA familiarity. However, we did not find any significant associations between ethnical similarity of the ECA and UVE outcomes. Importantly, the evidence displayed inconsistencies, particularly regarding the relationship between facial expressions exhibited by ECAs and UVE outcomes. Figure 2 not only summarizes the most important results in the included studies but also tries to extend them based on previous theories that can leverage our understanding of user-ECA-interaction.

This framework builds on the findings of the included studies in the present systematic review (Table 3), which informed our recommendations for reducing the UVE in user-ECA interaction (Table 4). To situate these results within a broader theoretical context, we draw on three key models: Cognitive Violation Theory (CEVT) (Burgoon and Hale, 1988; Burgoon and Walther, 1990; Kätsyri et al., 2015), the ABC model from the Rational Emotive Behavior Therapy (REBT) (Ellis et al., 2011; Turner, 2016) and the concept of the Uncanny Valley of Mind (UVM) (Gray and Wegner, 2012; Stein and Ohler, 2017). CEVT highlights how mismatches between user expectations and ECA behavior influence user perceptions, while the ABC model from REBT explains how such violations trigger emotional, physiological and behavioral responses from the user. UVM emphasizes the role of mind perception in human ECA-interaction and analyzes the interaction beyond the mere appearance of ECA.

To better understand the UVE in user-ECAs interaction, the present framework goes beyond an exclusive focus on the ECA’s features and considers the user’s experience as a central component. We propose a model in which the UVE emerges from the dynamic interplay between ECA features and individual user characteristics based on the results from our included studies on users factors and ECA features (see Figure 2). The interaction begins with a trigger, which is a specific feature of the ECA (e.g., clothing, facial expression, gesture or communication style) as depicted in the studies included in the systematic review (Bailey and Schloss, 2024; Hao et al., 2024; Volante et al., 2016). This trigger activates cognitive appraisals in the user, such as judgments about the ECA’s degree of anthropomorphism: “This ECA is like a human being.” Before the interaction even starts, however, users bring their own factors into the experience. Characteristics such as gender, age, previous experiences, and personality traits were explored in the included studies (Belda-Medina and Calvo-Ferrer, 2022; Lahav et al., 2020; Lisetti et al., 2004; Luo et al., 2023; Prendinger et al., 2006; Sajjadi et al., 2019) shape their expectations and influence how they interpret the ECA’s features. Following the initial appraisal, the user evaluates whether the ECA matches or mismatches their expectations. Given the human brain’s predictive nature, a match typically leads to attractiveness. However, individual traits can moderate this process. Users high in openness to experience may perceive an unexpected or mismatching ECA as both attractive (Lisetti et al., 2004) and uncanny, driven by curiosity (Zibrek et al., 2018). In contrast, users with high trait anxiety may respond to mismatching ECAs with uncanniness, potentially perceiving them as a threat.

It is essential to redefine the outcomes of the UVE across four key levels, grounded in validated theories of Psychology (Ellis et al., 2011). The first and most critical level is cognitive, or how the user thinks about the ECAs. For example, it is important to know whether they perceive it as competent or incompetent, friendly or unfriendly. These cognitive appraisals shape the second level, which is emotional. Here, we assess the user’s emotional response to the ECA, such as feeling relaxed, uncomfortable, surprised, curious, disgusted, or anxious. The third level involves physiological responses, such as changes in skin conductance or heart rate, which indicate levels of stress or relaxation. Finally, the fourth level is behavioral, where we examine how often the user maintains or even initiates the interaction with the ECA. Evaluating outcomes across all four levels is essential for a comprehensive understanding of user experience with ECAs.

In designing and evaluating human-ECA interactions, it is important to consider that users may naturally perceive and respond to ECAs as if they were human partners (Scheele et al., 2015). This opens the door for social cognition to play a role in these interactions with its well-known cognitive biases. One example is the hostile attribution bias, which is the tendency to see unclear behavior as hostile (Birch et al., 2025). In the context of ECAs, an ambiguous response might be misinterpreted negatively, as rude or even aggressive. Another example, anchoring bias causes users to rely too heavily on their first impression of the ECA, even if later behavior is different (Qi, 2024). Lastly, negativity bias means that users give more weight to negative experiences than to positive ones (Vaish et al., 2008). Thus, one awkward moment with the ECA can ruin the entire interaction. These well-known cognitive tendencies come from research on how humans relate to other people. A well-designed ECA should minimize ambiguity, promote trust from the start, and recover gracefully from small mistakes.

Several studies demonstrated that female users consistently rated ECAs as more attractive and reported lower levels of uncanniness compared to male users (Belda-Medina and Calvo-Ferrer, 2022; Lahav et al., 2020; Lisetti et al., 2004; Sajjadi et al., 2019). This may be attributed to higher levels of empathy in female users, which can positively influence their perception of ECAs. Men, on the other hand, often exhibit greater skepticism toward human-like ECAs, finding it more difficult to fully accept the agent’s anthropomorphic qualities. However, these findings should be interpreted with caution because some studies suggest that females are more likely to find ECAs attractive (Foster, 2007), while others argue the opposite, claiming males are more inclined to do so (Kuo et al., 2009). In terms of uncanniness, research suggests that female users may be more susceptible to feelings of uncanniness (MacDorman and Entezari, 2015), possibly due to a greater sensitivity to disgust compared to men (Tybur et al., 2011).

Additionally, younger participants, particularly those experiencing flow during the engagement, tended to perceive ECAs more favorably, with higher ratings of attractiveness and fewer reports of discomfort (Lisetti et al., 2004; Prendinger et al., 2006), In contrast, older users tend to be more sensitive to the UVE, likely due to a heightened awareness of the agent’s human-like but imperfect features. Contrary to our findings on human-ECA interactions, research on human-robot interaction shows that users aged 18–59 are more likely to find robots uncanny compared to older users (60–87 years) (Tu et al., 2020), which could be linked to enhanced emotion regulation skills in older individuals (Carstensen, 1995). Moreover, previous research shows that young children (ages 3–5) may not experience the UVE in the same way as other age groups (Tu et al., 2020; Brink et al., 2019).

Even the educational background makes a difference in which UVE studies should be mindful when pulling together different domains. While Social Sciences students may be less familiar with cutting-edge technical advancements, they may exhibit greater openness to engaging with ECAs. When asked whether they would apply career recommendations from their interactions with ECAs in their daily life, social sciences and humanities users were notably more likely to respond positively compared to users from exact sciences, medicine, and engineering. Based on this review, the ideal user profile for effective interaction with ECAs appears to be young, preferably Gen Z, females, preferably with a background in Social Sciences and a high level of openness to experience. These users generally show greater openness and comfort with ECAs, demonstrating a higher tolerance for minor imperfections in the agent’s behavior. Their social awareness enables smoother engagement with ECAs, significantly reducing the likelihood of experiencing the UVE.

However, these results should be interpreted with caution, because we had limited demographic categories to compare, especially when we investigated the educational background, where we found differences in users from just two educational areas: Social Sciences and Exact Sciences.

In our review, we observed that ECAs studied were female, half-body, and dynamic in approximately one-third of the cases. Full-body ECAs generally elicited higher levels of anthropomorphism but were also more prone to triggering the uncanniness feelings, especially when their motion was dynamic (Buttussi and Chittaro, 2019). Conversely, half-body and face-only ECAs, while less anthropomorphic, received lower uncanniness ratings (Conrad et al., 2015). Interestingly, studies using scripted ECAs confirmed the UVE in only 2 out of 9 cases (Conrad et al., 2015; Zheleva et al., 2023), suggesting that limited interactivity may reduce the likelihood of eliciting uncanniness responses.

Studies indicate that ECAs designed with complex communication features often yield more positive responses. For example, an ECA incorporating humor and playful comments about healthy eating was rated more favorably and improved users’ moods compared to a non-humorous counterpart delivering factual information; (Buttussi and Chittaro, 2019; Hao et al., 2024). ECAs that express emotions are perceived as more anthropomorphic and emotionally intelligent than those that maintain emotional neutrality. Positive messaging, characterized by a higher word count, fewer negative terms, and greater use of exclamation marks (e.g., “I’m happy to help!”), further enhances perceptions of attractiveness and emotional intelligence (Ham et al., 2024; Ter Stal et al., 2021). ECAs providing empathic and supportive feedback, such as “You did a good job! Please relax a bit. Then let us continue,” were seen as more engaging and enthusiastic compared to those that were purely task-oriented (Min et al., 2024). Users were nearly twice as likely to show acknowledgement responses, such as verbal affirmations like “yes” or “aha,” when the ECA displayed more facial expressions, including movements of the head, eyes, mouth, gaze direction, and subtle blinking patterns (Conrad et al., 2015). Previous literature showed that while expressing empathy can make ECAs more appealing (Parmar et al., 2022), it can simultaneously increase their uncanniness (Gray and Wegner, 2012; Stein and Ohler, 2017). This uncanniness is frequently intensified when ECAs exhibit anthropomorphic appearance, emotions or consciousness (Diel et al., 2021; Ho et al., 2008). Assigning mental abilities to ECAs, such as emotions or decision-making, exacerbates the risk of the UVE, as users tend to become less willing to interact with them (Yin et al., 2021). Adding to the complexity, some studies suggest that uncanny reactions are stronger when ECAs display basic sensations like hunger or pain (Gray and Wegner, 2012), while others argue that the effect is more pronounced with complex mental traits such as memory and moral judgment (Lu, 2021).

The non-verbal behaviors of ECAs play a crucial role in fostering user engagement. However, their timing is essential; if poorly synchronized or overly frequent, these behaviors may appear unnatural and potentially elicit feelings of uncanniness (Conrad et al., 2015). Therefore, increased engagement does not necessarily correlate with increased attractiveness or user comfort. Reactions to happy facial expressions of the ECAs were mixed. While many participants viewed them as friendlier and more cooperative; some perceived them as insincere or overly enthusiastic. On the other hand, negative expressions often led to perceptions of aggression or uncooperativeness, though a few users associated them with professionalism and seriousness (Luo et al., 2023). Smiling ECAs were generally rated as warmer and more cheerful than those without smiles (Min et al., 2024). Furthermore, users interacting with ECAs featuring rich non-verbal behaviors, such as responding to eye contact, pausing when users stopped looking, and addressing interruptions, reported higher enjoyment and rated these ECAs as more autonomous and natural compared to those with fewer non-verbal features (Conrad et al., 2015). An extroverted ECA, which actively initiated conversations and maintained eye contact 90% of the time, was perceived as more natural and engaging than an introverted ECA that made eye contact only 30% of the time (Prendinger and Ishizuka, 2001; Saad and Choura, 2022). However, results must be interpreted with caution, because these studies were rated as having either weak (Conrad et al., 2015; Luo et al., 2023; Min et al., 2024) or moderate overall methodological quality (Prendinger and Ishizuka, 2001; Saad and Choura, 2022).

The findings across the included studies suggest that mitigating the UVE requires ECAs to incorporate expressions of happiness, show concern, and exhibit social behaviors. Engaging in active listening, providing encouragement, and aligning emotional expressions with user expectations and context are crucial for creating a natural and satisfying experience. Customizing emotional displays based on user preferences, supported by insights from relevant datasets, such as the MuFaSAA Dataset, can significantly improve interaction quality and user satisfaction. Understanding user expectations, affective, cognitive, and behavioral, will aid in designing ECAs that are both effective and engaging. In response to these findings, we have developed a preliminary checklist focused on key design features of ECAs that may reduce the likelihood of UVE. This checklist is intended as a practical starting point for designers and developers working to improve the emotional and social realism of ECAs.

Considering our systematic review’s findings, we propose the following strategies to enhance the perceived attractiveness of the ECAs and reduce the likelihood of eliciting uncanny feelings. More details can be found in the Checklist for Avoiding the Uncanny Valley Effect in ECAs (see Table 4), which has been developed based on the findings of the present systematic review. By following our recommendations outlined below, designers and developers can reduce the likelihood of triggering the UVE in users.

ECA should adopt a positive attitude: To enhance their attractiveness, future ECAs should exhibit humor (for example, telling jokes or using a sarcastic tone) and friendliness (such as greeting users at the start of interactions or apologizing when unable to assist). In text-based communications, employing a greater volume of words, minimizing negative affect keywords, and increasing positive affect terms, along with the use of punctuation and exclamation marks, can make interactions more attractive (Ter Stal et al., 2021). For instance, phrases like “I am glad to assist you!” can increase the perceived attractiveness of ECAs (Ter Stal et al., 2021).

To make ECAs more attractive, it is essential to enhance both verbal and non-verbal communication. ECAs should not only provide clarifications during interactions, but also interpret cues from the user’s speech and actions to enrich the conversation (Volante et al., 2016). Demonstrating reflective listening, acknowledging the user’s emotions and providing empathetic responses, like “It seems you are feeling scared.”—helps foster a stronger emotional connection and boost user confidence (Schouten et al., 2017). Furthermore, ECAs need to respond to non-verbal cues effectively, such as showing confusion to dismissive gestures or attentiveness to pointing actions (Wang et al., 2021). Research shows that when ECAs mimic natural facial expressions and head movements, even while listening, user engagement improves, often leading to positive responses like increased smiling (Volante et al., 2016).

However, because not all users react the same way, understanding their expectations is also important. Using resources like the MuFaSAA Dataset (Dennler et al., 2023), which provides insights into user preferences, can help tailor these interactions. Adapting ECA behavior based on individual needs, as seen with the Geminoid HI robot, can lead to more effective and personalized interactions. To ensure ECAs meet user expectations and avoid the UVE, it’s important to use reliable metrics. Tools like the Negative Attitude Toward Robots Scale (NARS), the Robot Anxiety Scale (RAS), and measures such as reaction times during interactions can help developers understand user reactions. Aligning ECA behavior with the results from these metrics can prevent discomfort and increase user satisfaction, helping to ensure a positive experience and mitigate.

Future ECAs should offer extensive customization options, starting with gender preferences to enhance their perceived attractiveness. Our included studies presented mostly female ECAs, probably because previous literature shows a general preference for female ECAs (Kulms et al., 2011). Both male and female users generally showed a preference for female ECAs. However, female users tended to be more open to interacting with male or agender ECAs (Volante et al., 2016). Beyond physical features, future ECAs should not only allow for modifications in physical appearance but also offer personalization of personality traits. These traits can be showcased through their voice, facial expressions, and body movements (Ahmad et al., 2022; Sonlu et al., 2021). Users should be able to choose from a range of traits based on the Big Five Personality Model (McCrae and Costa, 1997). A study highlighted that ECAs exhibiting clear signs of extraversion verbally, with phrases like “Yes, I will purchase the return ticket immediately. Thank you, officer,” and showing happiness nonverbally, were perceived as more attractive by users (Ter Stal et al., 2021). Our systematic review suggests a preference for extraversion in ECAs, yet contrasting studies reveal nuanced findings. Research indicates that extroverted ECAs, characterized by quicker speech and more frequent smiles, were deemed less trustworthy than introverted ones by extroverted users (Liew and Tan, 2016; Loveys et al., 2020b). This highlights the diversity in user preferences regarding ECA personalities, underlining the importance of offering customizable traits to accommodate a wide range of expectations.

To prevent eliciting uncanny feelings, future ECAs should be capable of adjusting their emotional responses based on the social context. For instance, in competitive scenarios where the ECA emerges victorious, it should naturally display pride and happiness, even if it means the user participants lose. Likewise, during collaborative tasks where both the ECA and users succeed, the ECA should similarly exhibit feelings of pride and joy. A study investigating the effect of ECAs displaying incongruent emotions in a competitive gaming setting found that ECAs not showing happiness at their own victories led to perceptions of uncanniness among users (Volante et al., 2016). This underscores the importance of ECAs being emotionally in tune with the context to avoid unsettling reactions.

The UVE is still in its early stages but represents a promising area of research. One major issue is that the UVE has not been clearly defined, and many studies fail to meet minimal methodological standards. To address this, we provided a clear definition for each variable included in the UVE and expanded its scope to encompass not only the physical but also behavioral and mental features of ECAs. Another critical issue is that many of the studies we reviewed did not prioritize the UVE as a primary objective, leading to a lack of rigor and reproducibility. In response, we offered methodological recommendations drawn from experimental psychology, where higher standards of rigor and replicability are common. These recommendations should guide future research toward more robust, reliable findings in this field.

We strongly recommend that future studies move beyond focusing solely on ECA features and instead analyze the UVE in a way similar to how we assess interactions between humans. A continued focus on ECA features alone risks creating overly universal agents that fail to meet personalized needs, leading to low user engagement and poor usability. Future research should also explore the psychological characteristics of users, such as personality traits and clinical factors like anxiety or depression, which may increase the likelihood of experiencing uncanniness (MacDorman and Entezari, 2015). Conversely, conditions such as autism may reduce this effect (Feng et al., 2018). Additionally, studies should investigate contextual factors, such as optimal interaction durations to prevent cognitive overload, as well as appropriate tasks and environments that enhance user engagement with ECAs.

In human interactions, we analyze and predict others’ emotions based on a combination of sensory inputs, past experiences, and contextual factors (Barrett et al., 2011). This same process occurs when interacting with ECAs. Barrett’s theory of constructed emotion is particularly relevant for understanding the UVE because it highlights how users may perceive ECAs’ emotional expressions differently depending on the context. For instance, the same facial expression of the ECAs may be interpreted as welcoming in one setting but uncanny in another. As Barrett argues, when the context changes, so does the emotional interpretation. Future research on ECAs should consider how varying contexts might influence users’ perceptions of the agent’s emotions, which could either mitigate or exacerbate the UVE. In our framework, we analyzed the UVE by considering not only the user and the ECA, but also the context in which the interaction occurs.

There is a pressing need for more accurate methods of assessing the UVE. Nearly half of the included studies used a maximum of five items to measure the UVE, with many relying on single-item assessments, an approach that should no longer be considered acceptable (Sarstedt and Wilczynski, 2009). More nuanced and comprehensive questions are required, along with verification items and scales that have been thoroughly tested for reliability, such as Cronbach’s alpha. Currently, the most widely used tools are the Godspeed Questionnaire Series (Bartneck et al., 2009; Tobis et al., 2023) and the scale proposed by Ho and MacDorman that primarily rely on semantic differential items (e.g., humanlike-mechanical, friendly-hostile). While these instruments are practical and widely adopted, they raise important concerns. First, they predominantly assess perceptual impressions, with limited sensitivity to affective discomfort, ambivalence, or behavioral intentions to use the ECAs. Second, binary adjective pairs are prone to semantic ambiguity and cognitive noise, especially when the adjectives are polysemous. Additionally, these instruments are vulnerable to social desirability bias. Negatively valenced adjectives such as “awful,” “unpleasant,” or “incompetent” may be perceived as socially inappropriate, leading participants to underreport negative reactions. A particularly critical limitation is the lack of empirically established cut-off scores in the existing instruments. Without defined thresholds, it is not possible to determine with confidence when an ECA enters in the UVE zone. In clinical psychological research, cut-off scores are essential for converting continuous subjective ratings into interpretable categories with clinical relevance. For instance, the Brief Emotional Intelligence Scale (BEIS-10) and the Difficulties in Emotion Regulation Scale (DERS) include empirically derived thresholds to classify individuals along relevant dimensions, enabling more precise interpretation and application (Davies et al., 2011; Gratz and Roemer, 2015). Best practices in psychometric development emphasize a three-phase process: item generation, scale construction, and scale validation (Boateng et al., 2018). UVE research would benefit significantly from adopting such an approach to develop robust, multidimensional tools that measure anthropomorphism, attractiveness, and uncanniness as distinct yet related constructs. Critically, future tools should provide normative cut-offs to indicate mild, moderate, or severe UVE responses. Without such developments, the empirical study of user discomfort and avoidance in human-ECA interactions will continue to lack coherence and predictive power.

Moving forward, future research should aim to develop tools that better capture the emotional and social dimensions of the UVE. In this regard, it is crucial to integrate both quantitative and qualitative feedback, as these subjective methods provide valuable insights, but they are not sufficient on their own (Taschereau-Dumouchel et al., 2022). We must also incorporate behavioral (e.g., eye gaze) and physiological measurements (e.g., skin conductance), as some of the included studies have done (Appel et al., 2012; Bailey and Schloss, 2024; Hale and Hamilton, 2016; Schouten et al., 2017). These additional metrics can offer more precise information, allowing for a deeper understanding of the UVE. With these initiatives, UVE research can evolve toward a more comprehensive and accurate approach.

Based on our quality assessment, which revealed that most studies were of weak to moderate quality, we emphasize the critical need for methodological improvements in future research. To address this, we propose recommendations aligned with the CONSORT—Consolidated Standards of Reporting Trials (Baker et al., 2010; Eysenbach et al., 2011): CONSORT describes how the interaction with an ECA should be reported. It offers a clear checklist that can be used for randomized controlled trials (RCT), and also non-RCT evaluation reports (Eysenbach et al., 2011):