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Real conversations with artificial intelligence: A comparison between human-human conversations and human-chatbot conversations

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Abstract

This study analyzed how communication changes when people communicate with an intelligent agent as opposed to with another human. We compared 100 instant messaging conversations to 100 exchanges with the popular chatbot Cleverbot along seven dimensions: words per message, words per conversation, messages per conversation, word uniqueness, and use of profanity, shorthand, and emoticons. A MANOVA indicated that people communicated with the chatbot for longer durations (but with shorter messages) than they did with another human. Additionally, human-chatbot communication lacked much of the richness of vocabulary found in conversations among people, and exhibited greater profanity. These results suggest that while human language skills transfer easily to human-chatbot communication, there are notable differences in the content and quality of such conversations.

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... Claro, también ha habido críticas sobre el concepto "caja negra" y sus capacidades para entender a los algoritmos, especialmente porque reproducen una idea de "opacidad", aun cuando hay diferentes alternativas para visibilizarlos y entenderlos. Además, diferente a su uso convencional para referirse a tecnologías estables ya "empaquetadas o cerradas", los algoritmos son fluidos, maleables y cambiantes(BUCHER, 2016;GILLESPIE, 2013;SEAVER, 2013).En el caso de los chatbots, aunque ha habido una creciente literatura en el área, la mayoría de investigaciones solo analizan los factores que influencian la interacción humano-chatbot y las características de la misma desde abordajes psicológicos y experimentales(ARAUJO, 2018;HILL;FORD;FARRERAS, 2015;HO;HANCOCK;MINER, 2018;PÜTTEN et al., 2010;TAYLOR et al., 2014;XUETAO;BOUCHET;SANSONNET, 2009). ...
... Claro, también ha habido críticas sobre el concepto "caja negra" y sus capacidades para entender a los algoritmos, especialmente porque reproducen una idea de "opacidad", aun cuando hay diferentes alternativas para visibilizarlos y entenderlos. Además, diferente a su uso convencional para referirse a tecnologías estables ya "empaquetadas o cerradas", los algoritmos son fluidos, maleables y cambiantes(BUCHER, 2016;GILLESPIE, 2013;SEAVER, 2013).En el caso de los chatbots, aunque ha habido una creciente literatura en el área, la mayoría de investigaciones solo analizan los factores que influencian la interacción humano-chatbot y las características de la misma desde abordajes psicológicos y experimentales(ARAUJO, 2018;HILL;FORD;FARRERAS, 2015;HO;HANCOCK;MINER, 2018;PÜTTEN et al., 2010;TAYLOR et al., 2014;XUETAO;BOUCHET;SANSONNET, 2009). ...
... Claro, también ha habido críticas sobre el concepto "caja negra" y sus capacidades para entender a los algoritmos, especialmente porque reproducen una idea de "opacidad", aun cuando hay diferentes alternativas para visibilizarlos y entenderlos. Además, diferente a su uso convencional para referirse a tecnologías estables ya "empaquetadas o cerradas", los algoritmos son fluidos, maleables y cambiantes(BUCHER, 2016;GILLESPIE, 2013;SEAVER, 2013).En el caso de los chatbots, aunque ha habido una creciente literatura en el área, la mayoría de investigaciones solo analizan los factores que influencian la interacción humano-chatbot y las características de la misma desde abordajes psicológicos y experimentales(ARAUJO, 2018;HILL;FORD;FARRERAS, 2015;HO;HANCOCK;MINER, 2018;PÜTTEN et al., 2010;TAYLOR et al., 2014;XUETAO;BOUCHET;SANSONNET, 2009). ...
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... There is also the enduring popularity of Eliza, which is still used online (Heller et al., 2005) decades after its inception. Both observations are as true for early chatbot interaction as it is today, with researchers highlighting the persistent communication with chatbots many individuals choose to undertake (Hill, Ford, & Farreras, 2015). ...
... While this evidence was hopeful, it failed to push chatbots into the clear category of broadly useful for language practice. In contrast, Hill et al. (2015) analysed 100 messaging conversations and found that humans carried on significantly longer messaging conversations with the chatbot than with other humans. ...
... These results were not a good sign for the potential usefulness of chatbots for stimulating meaningful interest in language learning. However, the longstanding (Fryer & Carpenter, 2006) and recent (Hill et al., 2015) empirical evidence pointing towards sustained human interest in talking to chatbots suggests that a more fine-grained examination is necessary. Given the essential role of students' prior language competence, such an examination must take students' language skills into consideration. ...
Article
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... There have been many studies which have looked into the many different factors which affect the perception of humanness by users, such as reciprocity, expressions of emotion, etc. [20,32,26,35]. The absolute majority of those studies are concentrated in how content influences the machine-likeness of a conversational system. ...
... Brennan et al. [6] tested the effects of message style on dialog and on people's mental models of computer agents. Hill [20] compared human-human online conversations and human conversations with the chatbot Cleverbot. Researchers found that people adapt their communication styles accordingly to the other conversant, regardless they are a machine or human. ...
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... Studies have been conducted on how people react to agents and avatars, in an attempt to establish why users have a social reaction towards them regardless of the knowledge that they are conversing with a machine. The authors [11] and [12] investigated the nature of the changes in people's communication when they interact with intelligent agents, as compared to human-to-human communication. ...
... 78% of the students found the avatar's tone of voice either very appealing, quite appealing or moderately appealing. This was unexpected, as we were not entirely satisfied with the quality of the avatar's voice and previous research [11], [12], [13] has found that subjects respond to artificial voices in the same way as they do with real human voices. ...
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... Studies have been conducted on how people react to agents and avatars, in an attempt to establish why users have a social reaction towards them regardless of the knowledge that they are conversing with a machine. The authors [11] and [12] investigated the nature of the changes in people's communication when they interact with intelligent agents, as compared to human-to-human communication. ...
... 78% of the students found the avatar's tone of voice either very appealing, quite appealing or moderately appealing. This was unexpected, as we were not entirely satisfied with the quality of the avatar's voice and previous research [11], [12], [13] has found that subjects respond to artificial voices in the same way as they do with real human voices. ...
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... [59]). The reviewed studies also showed that AI-enabled feedback learning systems improved students' problem-solving skills [65], communication skills [66], self-directed learning capacity [67], and creativity [68]. AI chatbots enhance students' confidence and reduce their anxiety regarding English learning [69] [44] . ...
Chapter
AI impacts individual students’ learning, schools and higher education institutions, curriculum design, and national and international educational policy-making. However, the holistic picture has been difficult to draw. Therefore, this chapter aims to analyze how AI is influencing teaching and learning at different levels of the educational ecosystem. The study is literature-based and focuses on the following questions: What does the latest education research literature describe as major findings when AI has been applied in teaching and learning? and How does AI impact different levels of the educational ecosystem and what kind of development is to make AI a meaningful tool in education and learning? We have a lot of evidence that AI has affected positive outcomes in cognitive learning, increased students’ motivation and engagement in learning, and helped students with socio-emotional challenges by increasing self-confidence and encouraging students with supportive feedback. Conversational AI tools have provided new opportunities for dynamic dialogue through generative AI. We also have many AI tools for teachers’ work in classrooms and for their development. The curriculum process and AI literacy have taken the first steps both in schools and higher education. Despite these positive observations, many challenges still exist. Most of them are related to the whole ecosystem. Micro, meso, and macro levels are working separately, and there is a lack of connections between and within levels. The big challenge is also that connections between the educational ecosystem and technological providers need to be stronger and functioning. In the discussion the recommendations for the future are presented: Overcoming the distance between policy and practice, more co-creation between technological designers, researchers, and practitioners, more focus on long-term impact, and clarification of ethical issues throughout the educational ecosystem.
... Chatbots use artificial intelligence which uses human language for coding and decoding to understand and respond to interactions. 16 Chatbots are designed with an objective to immediately respond to customers' queries. This coupled with great convenience led to technology adoption. ...
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Artificial intelligence empowered Chatbots are altering the nature of service interfaces which has further resulted in raised expectations from Chatbots to understand customer's social interactions and respond them within the turnaround time. To close this research gap, we conduct an exploratory study in two phases‐industry's perspective and B2B customer's perspective and analyze results with the help of NVIVO 12 plus and Leximancer. The findings reveal perceived risk with respect to Chatbots is high, complex pricing structure along with nonavailability of testing options makes the pre purchase more complex. Moreover, interactive speed, customization especially with respect to language issues, integration with other platforms is some of the major themes which influence customer experience. Advancements in AI, natural language processing and more testing at all phases will bring efficiency, automation first strategies. Further, our findings suggest Chatbots must provide more personalization, scalability and omni channel engagement and focus on delivering more enhanced customer experience. Chatbots must offer a grievance management dashboard where the customer can see live queries, resolved queries, present queries status and so on to get transparency. Chatbots streamline the lead qualification process, greatly improve, and speed up the data collection therefore, enhancing customer experience.
... Diwani (2019) expressed that the use of virtual assistants is that they're highly flexible, easy to use, and valuable across a variety of functions and departments. Hill et al. (2015) made a research on change of communication that happens while communicating with a chatbot compared to a human which showed that messages sent to chatbots has less words when comparison is made with human. It showed that people adopting chatbots feel more confident and comfortable as compared to talking with humans due to more customized and fast responses. ...
Article
The purpose of this research paper is to determine the true picture of chatbot system with respect to buying behaviour of customer. Four factors related to TAM model of chatbot system namely Perceived Usefulness, Security, Attitude and Ease of Use are considered as independent variables, while intention related to buying of product is considered as dependent variable. All the factors were found to be contributing to the buying intention. Non probability judgmental sampling method was used for the collection of primary data from 924 respondents. Various machine learning algorithms including gradient boosting, random forest, logistic regression, naïve bayes and decision tree and were used to develop, train and test the model. Highest accuracy was found to be from the random forest and gradient boosting algorithm.
... ‫(شكؿ‬ 3 ‫ا‬ ‫العدهٓة‬ ‫الفرضٓة‬ ‫فض‬ ‫ر‬ ‫تـ‬ ‫فقد‬ ، ) ‫لٍا،‬ ‫البدٓمة‬ ‫الفرضٓة‬ ‫كقبكؿ‬ ‫ئٓسٓة‬ ‫لر‬ ( ‫الدٚلة‬ ‫هستكل‬ ‫عىد‬ ‫إحصائٓة‬ ‫دٚلة‬ ‫ذات‬ ‫ٓة‬ ‫تأثٓر‬ ‫عٛقة‬ ‫تكجد‬ ‫أىً‬ ‫أم‬ α ≤ 0.05 ‫ة‬ ‫(القدر‬ ‫خصائص‬ ‫هابٓف‬ ) ‫كرضا‬ ‫هستقمة‬ ‫ات‬ ‫كهتغٓر‬ ‫الكفاءة)‬ ‫الهحادثة؛‬ ‫عمِ‬ ‫ة‬ ‫القدر‬ ‫شخصٓة؛‬ ‫أك‬ ‫ٌكٓة‬ ‫بىاء‬ ‫العاطفة؛‬ ‫إظٍار‬ ‫عمِ‬ ‫كه‬ ‫التفاعمٓة‬ ‫الدردشة‬ ‫ىاهج‬ ‫بر‬ ‫عف‬ ‫العهٛء‬ ‫الىحك‬ ‫عمِ‬ ‫التأثٓر‬ ‫أٌهٓة‬ ‫حٓث‬ ‫هف‬ ‫اهؿ‬ ‫العك‬ ‫تٓب‬ ‫تر‬ ‫ككاف‬ ‫تابع‬ ‫تغٓر‬ ‫اظٍرت‬ ‫حٓث‬ ‫الهحادثة).‬ ‫عمِ‬ ‫ة‬ ‫القدر‬ ‫شخصٓة؛‬ ‫أك‬ ‫ٌكٓة‬ ‫بىاء‬ ‫الكفاءة،‬ ‫العاطفة؛‬ ‫إظٍار‬ ‫عمِ‬ ‫ة‬ ‫التالِ(القدر‬ ‫العاطفة‬ ‫إظٍار‬ ‫عمِ‬ ‫ة‬ ‫القدر‬ ‫لخصائص‬ ‫آجابِ‬ ‫تاثٓر‬ ‫كجكد‬ ‫عمِ‬ ‫أكدت‬ ‫التِ‬ ‫اسات‬ ‫الدر‬ ‫بعض‬ ‫ىتائج‬ ‫هع‬ ‫افؽ‬ ‫تك‬Radziwill & Benton, 2017) ‫شخصٓة‬ ‫أك‬ ‫ٌكٓة‬ ‫بىاء‬ ‫)؛‬(Baylor & Rosenberg-Kima, 2003;Kerly, 2007;Radziwill & Benton, 2017; Knight,2017; Staven, 2017) ( ‫الهحادثة‬ ‫عمِ‬ ‫ة‬ ‫القدر‬ ‫)؛‬Farreras et al., 2015;Hill et al., 2015; Knight,2017 ( ‫الكفاءة‬ ‫)؛‬ Schlicht, 2016;Taylor ,2016;Van Manen,2016; Wei, 2016 ‫التفاعمٓة.‬ ‫الدردشة‬ ‫اهج‬ ‫لبر‬ ‫الهستخدهٓف‬ ‫رضا‬ ‫عمِ‬ ) ‫اختبار‬ ‫ىتائج‬ ‫أظٍرت‬ ‫قد‬ Independent sample t test ‫ه‬ ‫عٓىتٓف‬ ‫هتكسطْ‬ ‫بٓف‬ ‫لمفرؽ‬ ‫لمىكع‬ ‫ستقمتٓف‬ ‫دٚلة‬ ‫هستكل‬ ‫عىد‬ ‫إحصائٓة‬ ‫دٚلة‬ ‫ذات‬ ‫فركقات‬ ‫تكجد‬ ‫بأىً‬ ‫(الذككر,ا٘ىاث)‬ 0.05 ‫الهبحكثٓف‬ ‫استجابة‬ ‫فْ‬ ( ‫الجدكؿ‬ ‫فْ‬ ‫هبٓىة‬ ‫الىتائج‬ ‫ك‬ ‫الىكع‬ ‫لهتغٓر‬ ‫تعزل‬ 3 ‫ٓبٓف‬ ‫الذم‬ ‫ك‬ ) ‫قٓهة‬ ‫أف‬ T ‫اسة‬ ‫الدر‬ ‫أبعاد‬ ‫لجهٓع‬ ‫بالىسبة‬ ‫سالبة‬ ‫الدٚلة‬ ‫هستكل‬ ‫قٓهة‬ ‫كذلؾ‬ P ‫هف‬ ‫أقؿ‬ 0.05 ‫اٖ‬ ‫لكافة‬ ‫لهتغٓر‬ ‫تعزل‬ ‫ٓة‬ ‫جكٌر‬ ‫فركؽ‬ ‫كجكد‬ ‫ٓعىْ‬ ‫هها‬ ‫أٓضا‬ ‫بعاد‬ ‫دٚلة‬ ‫هستكل‬ ‫عىد‬ ‫الجىس‬ 0.05 ‫عىد‬ ‫إحصائٓة‬ ‫دٚلة‬ ‫ذات‬ ‫فركؽ‬ ‫ٌىاؾ‬ ‫أف‬ ‫أم‬ ‫البدٓمة‬ ‫الفرضٓة‬ ‫قبكؿ‬ ‫كبالتالْ‬ ‫هستكل‬ 0.05 ‫الجىس‬ ‫لهتغٓر‬ ‫تعزل‬ ‫الهبحكثٓف‬ ‫استجابة‬ ‫فْ‬ , ‫ىتائج‬ ‫هع‬ ‫ٓتفؽ‬ ‫ها‬ ‫كٌك‬ ‫ا٘ىاث‬ ‫كلصالح‬ ( ‫السابقة‬ ‫اسات‬ ‫الدر‬ ...
... Contrary to our approach focused on platform comparison, Kuligowska (2015) focuses on the comparison of particular chatbot deployments. Hill et al. (2015) focus on an analysis of differences between human-chatbot conversation and humanhuman conversation. This study analyzed seven variables (words per conversation, messages per conversation, average number of words per message, etc.) from real human-human and humanchatbot conversations. ...
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Chatbots are going to be the main tool for automated conversations with customers. Still, there is no consistent methodology for choosing a suitable chatbot platform for a particular business. This paper proposes a new method for chatbot platform evaluation. To describe the current state of chatbot platforms, two high-level approaches to chatbot platform design are discussed and compared. WYSIWYG platforms aim to simplicity but may lack some advanced features. All-purpose chatbot platforms require extensive technical skills and are more expensive but give their users more freedom in chatbot design. We provide an evaluation of six major chatbot solutions. The proposed method for the chatbot selection is demonstrated on two sample businesses – a large bank and a small taxi service.
... Kalman and Gergle, 2010) or CMC features (e.g., Hill et al., 2015). Thus, we followed these propositions and assigned all social cues created by visual and text-based elements, such as typefaces and emoticons, to the newly developed fourth visual cue category called CMC cues. ...
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Conversational agents (CAs) are software-based systems designed to interact with humans using natural language and have attracted considerable research interest in recent years. Following the Computers Are Social Actors paradigm, many studies have shown that humans react socially to CAs when they display social cues such as small talk, gender, age, gestures, or facial expressions. However, research on social cues for CAs is scattered across different fields, often using their specific terminology, which makes it challenging to identify, classify, and accumulate existing knowledge. To address this problem, we conducted a systematic literature review to identify an initial set of social cues of CAs from existing research. Building on classifications from interpersonal communication theory, we developed a taxonomy that classifies the identified social cues into four major categories (i.e., verbal, visual, auditory, invisible) and ten subcategories. Subsequently, we evaluated the mapping between the identified social cues and the categories using a card sorting approach in order to verify that the taxonomy is natural, simple, and parsimonious. Finally, we demonstrate the usefulness of the taxonomy by classifying a broader and more generic set of social cues of CAs from existing research and practice. Our main contribution is a comprehensive taxonomy of social cues for CAs. For researchers, the taxonomy helps to systematically classify research about social cues into one of the taxonomy's categories and corresponding subcategories. Therefore, it builds a bridge between different research fields and provides a starting point for interdisciplinary research and knowledge accumulation. For practitioners, the taxonomy provides a systematic overview of relevant categories of social cues in order to identify, implement, and test their effects in the design of a CA.
... Recent systems are perceived to be more competent at interaction than classics, like ELIZA. Other research suggests that we draw upon our human communication skills when interacting with chatbots (Hill, Ford, & Farreras, 2015), with adaptations for word choice and length of conversations. Thus, if people are treating interactions with chatbots as they would interactions with humans, then the theories of human communication should be relevant to this HRI. ...
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The way humans and robots interact is no longer in the realm of fiction, because robots have seemingly gained the ability to respond to others, including humans. Communication research can be at the forefront of examining these interactions. This study used a “chatbot”—run by a researcher—to respond to a pre-generated set of questions asked by participants. An experiment varied the presence of typos and capitalized words in the responses. Significant main effects were found for typos in the responses, which had a negative effect on the perceived humanness of the respondent, as well as other perceptions. These findings, limitations, and directions for future research are discussed.
... These interactions can occur through speech or writing in the natural language, through motion sensors, interaction with devices, and in other ways. According to Hill et al. (2015), humans can easily adapt their language to human-chatbot communication, although there are notable differences in the content and quality of these conversations. Results presented by the authors show that people communicate with chatbots for longer periods, but with shorter messages when compared to human conversation. ...
Conference Paper
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Mobile devices advanced adoption has fostered the creation of various messaging applications providing convenience and practicality in general communication. In this sense, new technologies arise bringing automatic, continuous and intelligent features for communication through messaging applications by using web robots, also called Chatbots. Those are computer programs that simulate a real conversation between humans to answer questions or do tasks, giving the impression that the person is talking to someone else and not with a computer program. For agricultural purposes, it is important that the data about field conditions, such as air and soil temperature, air relative humidity, soil moisture, rainfall, wind speed and other relevant variables, be rapid and easily available for use by farm management systems, by specialists, or the farmer itself in decision-making processes. AgronomoBot was developed focused on the search and display of data acquired from a Wireless Sensor Network deployed on a vineyard. It is based on Telegram Bot API and is able to access information collected by eKo field sensors, bringing it back to a user through interaction over the Telegram application. The IBM Watson cognition services platform was also used for improving the user experience by enabling the use of natural language during the conversation experience, providing intention detection. Further developments are planned for AgronomoBot, such as the expansion to other messaging platforms, the implementation of speech communication capacity, image classification and continuous data analysis. It is hoped that with analytical capacity over the mass of available data, it becomes possible to work towards the prevention of harmful situations to agricultural productions, early detection of diseases in crops, energy and water waste reduction, and advanced management capabilities for the farmer.
... Compared to other Web 2.0 recommendation channels or platforms which lack proactivity, instantaneity, natural language and the use of conversational protocols similar to consumer thinking (Kerly et al., 2007;Hill et al., 2015), chatbots are not only presented as one more recommendation channel. They also adopt a role of "reliable influencer" precisely because in their conversational protocols they use similar thought patterns to the one that the consumer establishes in the choice of a restaurant and offering very adjusted recommendations. ...
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Consumers use the Internet to obtain information on tourism products and services. When evaluating the alternatives, they are faced with a large volume of information that makes their purchasing decision difficult. In this context, the generalized use of mobile instant messaging (MIM) has led to the implementation of chatbots in these channels, to help to plan the purchase. This research explores restaurant selection through a WhatsApp mobile instant messaging (MIM) chatbot. A study is made of the channels consulted by travellers on Web 2.0 as well as the search models and restaurant selection processes, and a case study is presented. The results allow the diagnosis of the main criteria of user behaviour in this type of conversational interface in the decision-making process related to gastronomic consumption.
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The purpose of this study was to investigate student perceptions and acceptance of a rule-based educational chatbot in higher education, employing the TAM (Technology Acceptance Model) framework. The researchers developed a rule-based chatbot for this purpose and examined the students' technology acceptance using qualitative research methods. Therefore, the study was design-based research using qualitative research methods. The participants of the study comprised 22 students studying in the Science Teaching program of Trakya University Faculty of Education and enrolled in the Modern Physics Course in the 2021–2022 fall semester. The research revealed that students' technology acceptance towards rule-based chatbots was high, even though these chatbots had technological limitations when compared to machine learning or deep learning-based ones. The students found rule-based chatbots to be useful, especially in terms of response quality, information quality, and access. Additionally, some technical details and open-source codes were also presented in the study, which can be a guide for rule-based chatbots to be designed for other areas of education.
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Artificial intelligence technologies are rapidly advancing. As part of this development, large language models (LLMs) are increasingly being used when humans interact with systems based on artificial intelligence (AI), posing both new opportunities and challenges. When interacting with LLM-based AI system in a goal-directed manner, prompt engineering has evolved as a skill of formulating precise and well-structured instructions to elicit desired responses or information from the LLM, optimizing the effectiveness of the interaction. However, research on the perspectives of non-experts using LLM-based AI systems through prompt engineering and on how AI literacy affects prompting behavior is lacking. This aspect is particularly important when considering the implications of LLMs in the context of higher education. In this present study, we address this issue, introduce a skill-based approach to prompt engineering, and explicitly consider the role of non-experts' AI literacy (students) in their prompt engineering skills. We also provide qualitative insights into students’ intuitive behaviors towards LLM-based AI systems. The results show that higher-quality prompt engineering skills predict the quality of LLM output, suggesting that prompt engineering is indeed a required skill for the goal-directed use of generative AI tools. In addition, the results show that certain aspects of AI literacy can play a role in higher quality prompt engineering and targeted adaptation of LLMs within education. We, therefore, argue for the integration of AI educational content into current curricula to enable a hybrid intelligent society in which students can effectively use generative AI tools such as ChatGPT.
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