dataesr/scientific-paragraphs-categorization huggingface dataset:
It did a [good/great/amazing] job, (certainly) (way) better than I would have done. " Whereas many of those initially circulated anecdotes relate to relatively straightforward roles of content production such as text producer, language editor and translator, there are examples of how ChatGPT can also contribute to hybrid teams in more sophisticated roles. 2. 1. 5. 3. Roles of ChatGPT. One of my EMBA students shared their experience with ChatGPT: "In my interactions with ChatGPT, it felt as if I'm interacting with a team member via a chat function. […]  I received an immediate response and I felt I could trust the information that was given to me. […] I felt emotionally supported, I was so worried that I'm falling behind schedule and in using ChatGPT I actually felt a sense of relief. In a way, it is scary, isn't it? Could this type of interactions replace group work?".
Therefore, it is essential to create two types of tasks:  (1)  tasks with no access to tools like ChatGPT (like a math exam without calculators allowed) and (2) tasks where tools like ChatGPT are explicitly integrated (e. g. , homework). For the first type, there is no need to change much. However, it can only be tested in a controlled environment. It is not feasible to use AI to check for the use of AI, despite attempts to develop such checkers. For the second type, significant changes are needed. Open-ended homework assignments where students need to write text cannot be used anymore. Instead, one should think of tasks that require a combination of both or that involve an oral examination afterward. Interestingly, ChatGPT is not limited to natural language. ChatGPT also speaks several programming and querying languages, e.
Depending on the task and question, it can give useful, funny, or just wrong and misleading answers. While the latter is expected to be reduced over time considering that the training datasets will continue growing, the responsibility remains with the human to evaluate and assess these responses. Hence, we need to rethink how we design learning in order for learners to develop soft skills, such as critical thinking, complex problem solving, creativity and decision-making, that takes into account various teaching and learning spaces (e. g. , classrooms, meeting rooms, the industry) using new digital resources  (Pappas & Giannakos, 2021). Finally, using technologies like ChatGPT can be fun as it changes the way we interact with the computer, raising the need for new studies on how both cognitive and affective factors shape our behaviours towards more advanced chatbots and "truly" conversational agents.
Furthermore, for customers it will be largely indistinguishable whether they are served by a digital service provider or a human employee, and they are likely not to care very much. One can even expect that the instant availably (i. e. , no waiting for a customer contact agent to become available), 24/7 service, and in ones preferred language will be preferred by most customers over having to dial into a traditional call centre. Furthermore, the cost implications are significant, and given that we live largely in competitive market economies, these cost savings are likely to be competed away, leading to increasing standards of living. As we are at the beginning of this technological revolution of customer service, research is needed to understand better the key value drivers of ChatGPT and other generative AI solutions for the service sector.
The table also shows indicative solutions and reflective tactics for every strategy. Indicative solutions explain the operationalization of ChatGPT in the respective marketing strategy, and reflective tactics explain the tactical move marketers should follow to leverage ChatGPT and GPT-3. 0 to their fullest potential. ChatGPT has similar reflections of search engine optimisation and content marketing flavour. However, ChatGPT, to our knowledge, does not work same as Google. It has a trained dataset with a 570 GB equivalent size  (Theconversation, 2023). Moreover, the learning competence of GPT is expected to grow in the coming days. Unlike other search engines or content curation platforms, ChatGPT can precisely answer queries. Of all the benefits that ChatGPT can provide to marketers, it is paramount that company chatbots and content marketing teams should leverage the fullest potential of GPT-3 and its associated tools.
Challenges for marketers. While ChatGPT and GPT-3. 0 look promising for marketers, there are some challenges marketers have to face. ChatGPT can be an effective content curation tool similar to Google. Still, GPT -3 uses robust database models with AI-based learning algorithms, which indicates that keyword-based content or search engine marketing may not be effective in ChatGPT architecture. Given that the AI pre-trained algorithms are more powerful in the ChatGPT, marketing strategies require a well-defined collaboration with OpenAI. Some companies collaborate with OpenAI to use GPT-3. 0 for their tools, such as  Algolia, Copy. ai, Replier. ai, Jasper AI, and Quickchat (Bhattacharyya, 2023). Though the adoption of GPT-3. 0 will increase in the coming days, the financial and knowledge capital required for its implementation can be huge.
Financial services provision Consumers will always need the banks to provide financial services  (Soetan et al. , 2021) , there will always be reasons to engage -and with these engagements, data are being generated which are beneficial to both parties  (Mogaji et al. , 2020b). There lies an opportunity for ChatGPT to explore these datasets to inform some banking decisions. At this stage of the engagement, banks are inviting consumers to 'come bank with them', banks need to show some form of understanding, commitment, and awareness about the needs of their customers  (Sheth et al. , 2022; Omoge et al. , 2022). Banks can use ChatGPT technology for their front-end operations to enhance business operations and directly engage with customers. This conversation agent builds on the chatbot's success, which has been well integrated into banking operations; the chatbot is used to enhancing customers' experiences, providing answers to frequently asked questions, and streaming the banking relationship  (Abdulquadri et al.
ChatGPT empowers supercharges the generation of coherent and contextually appropriate responses to natural language inputs. This technology will revolutionise search and will transform the nature of access to products and services across industries. Naturally Google as a service was challenged, not only because search is changing by nature, but also because its own AI service underperformed whilst Bing resurfaced with a stronger AI proposition. Google effectively searches for web pages that include key phrases used for search. Google's search algorithm is designed to use keywords and phrases entered by the user to return a list of web pages, images, and other content that match the query. ChatGPT uses a language generation model and the Transformer architecture developed by OpenAI to search across a massive corpus of text data and to amalgamate comprehensive human-like text answers based on the input it receives.
It is designed to respond to natural language queries in a conversational manner and can answer questions, summarise information, and generate a comprehensive text. Both technologies can be useful for answering questions although they are designed for different purposes. Naturally, travellers (demand) and Tourism, Travel, Transport and Hospitality organisations and destinations (supply) are excited about the incredible potential the AI, ML and ChatGPT tools bring into the ecosystem, as illustrated in Table  3. On the demand side, traveller information is critical for their satisfaction. They search for information, construct itineraries, select suitable timetables, and evaluate alternative products and services. Tourism by definition is a hostile industry, as people/customers travel to unfamiliar destinations to cocreate experiences  (Buhalis et al.
Fig. 6  illustrates suggestions for Greek gluten-free recipes. Overall, ChatGPT can help Tourism, Travel, Transport and Hospitality organisations to revolutionise customer communication, improve service and enhance the travel experience, streamline operations and have access to knowledge databanks. The virality of ChatGPT instantly illustrated the tremendous potential of AI and ML. However, it also brought to light that language models like ChatGPT are complex and continuously evolving systems, so there is always room for improvement and further development. A range of issues related to intellectual property and ownership of data was also exposed. There are challenges ahead to improve the system by using more diverse training data, including text from different languages and cultures; understanding a more diverse set of users; providing clearer fallback responses; developing the depth and breadth of information provided; and avoiding confusing or misleading answers.
Incorporating more external knowledge sources, such as databases or APIs, usergenerated content, and user feedback into ChatGPT's response generation process can help it provide more accurate, in-depth and up-to-date information. Analysing big data faster and incorporating real-time data will assist language models to learn faster and have a bigger impact on the industry  (Buhalis & Volchek, 2021; Stylos et al. , 2021). Nevertheless, it is evident that this revolutionary technology will be assisting humanity to interact faster, cheaper and more efficiently cocreating value in the future. But it should also be noted that ChatGPT can also be used to generate fake reviews on hospitality and tourism organizations.
Further, generative AI can offer new mapping that is created because it can examine the corpus of the literature, unlike manual database searches. Watson and Webster state that the goal of the literature review is to provide a meta-analytic and that "the fundamental problem is that knowledge is not encoded, and scholars must rely on the methods of their forebears (reading or scanning many papers) to take a step forward. "  (Watson and Webster, 2020, pg. 9). Knowledge is now being encoded at a scale never seen before. Because of this process, generative AI can now identify related concepts iteratively with the researcher thus accelerating the research process. Developing a literature review is one of many steps along the research process that will benefit greatly from generative AI. Analysis of the data including surfacing second-order patterns will certainly also benefit from this technology.
If well designed and integrated with existing computer systems, AI chatbots may also reduce the workload of educators, administrators, and university management by supporting students which would enable them to focus more on pedagogical research, curriculum development, and innovation in higher education. Examples of commercially available chatbots used in some universities in the United States and United Kingdom include IBM's Watson (IBM, 2023) and Amazon's QnABot  (Strahan & Gleason, 2019) , while others are under development  (EDUBOTS, 2022). A new AI chatbot called ChatGPT, based on a large language model, was launched by a commercial company, OpenAI, in November 2022 as a follow up to a previously developed chatbot  (OpenAI, 2023). It utilises reinforcement learning algorithms to optimise the language model with human feedback, having been originally trained using human AI trainers who generated a range of dialogue that was fine-tuned using supervised learning and ranked to develop a reward model.
Reports indicate that the chatbot was trained on a large dataset (40 GB in size) from text on the Internet, equivalent to 133 million pages of text which would take a human being approximately 2200 years to read  (Welsh, 2023). Although the inner workings of ChatGPT are not fully transparent, OpenAI do acknowledge some of the limitations of the chatbot and others are actively interacting and experimenting with it to identify more  (Else, 2023). The implications of ChatGPT and other types of open AI platforms in higher education are hotly debated. Some argue they could be used to assimilate knowledge quickly which might benefit learning, but these platforms could also be used by students to write assessments and dissertations which raises plagiarism and academic integrity issues (Stokel-Walker, 2022).
There could also be long-term repercussions if students choose to by-pass reading about a topic in-depth and critically analysing different facets of it, using ChatGPT for rapid and potentially superficial learning. This could stifle critical thinking, problem solving, and creativity which are key skills to develop as they are needed in many professional careers  (O'Connor, 2023). Similarly challenges with ChatGPT exist in relation to the conduct and reporting of pedagogical research. These types of AI tools could be utilised throughout the research process to pose hypotheses, design experiments, write manuscripts, and understand scientific results  (Thorp, 2023) , which could be beneficial in resource limited settings where funding and human expertise may be limited. Although this could accelerate educational research by letting automated tools perform many routine research tasks, the responses generated by this chatbot while well written are not guaranteed to be accurate, with some who have used the platform identifying problems with incomplete citations or citations to research studies that do not exist  (Kendrick, 2023) , highlighting some of the shortcomings of the large language model.
While these may be solved in time as the AI model learns more about the nuances of human language and the sources of information on the Internet, the use of chatbots like ChatGPT in educational research poses other ethical issues. Trust and transparency could be challenging if chatbots are used to write scientific research studies (Lucy & Dowling, 2023), privacy and security may be another issue depending on the types of data entered into an open AI platform, as can the design and "persona" of the virtual conversational agent itself as it could unwittingly encourage inappropriate behaviour when interacting with researchers  (Okonkwo & Ade-Ibijola, 2021). An example of this can be seen in Microsoft's Tay bot which began to post offensive content on social media when released on Twitter in 2016  (Reese, 2016).
Secondly, ChatGPT can only provide answers based on previously documented knowledge used for its training. Thirdly, it requires human intelligence to generate the knowledge that can be used to train ChatGPT. And finally, conducting research still requires human intelligence as ChatGPT is not capable of doing so yet. This shift in focus from text writing to doing research highlights the evolution of academic work. It's important to note that writing text may no longer be a crucial component of scholarly work, as this task can be supported more efficiently by tools like ChatGPT. Text serves as a means to communicate the results of our research in a clear and accessible manner. Many scholars, including myself, have already used tools like Grammarly, spell checks, and professional proofreading to enhance their writing as English is not their native language.
It is important to notice that the effectiveness of these models may depend on the specific task, the dataset and the quality of the training data. ChatGPT has been used for a variety of NLP tasks, including language translation, question answering, and text summarization. It has also been used in the field of education to support and enhance the learning experience for students. ChatGPT can be used to provide personalised support to students, automate administrative tasks, support language learners, and enhance online education. However, it is important to remember that the use of ChatGPT and other AI models in education should be done with caution. The ethical and societal implications of the use of such automated tools must be considered before accepting the tools as an assistant for the student in their learning process.
Language learning support: ChatGPT can be used to support language learners by providing personalised feedback on grammar and vocabulary, and by assisting with language translation in a classroom setting. It can support language learners by giving them extra practice and feedback on their language abilities Enhancing online education: ChatGPT can be used to enhance online education. It can be used to improve online learning by giving students more tools and resources, as well as by making the learning experiences more interesting and participatory Individualised Support: ChatGPT can be used to provide one-on-one tutoring for students, by answering questions and providing explanations on various subjects. It may determine the student's comprehension level and offer explanations and tasks that are suitable for them.
The use of AI and NLP models like ChatGPT in education is still a growing field, and there might be other opportunities to be discovered as the technology and the understanding of its capabilities evolve. 4. 2. 5. 2. Challenges. Despite the potential benefits, there are concerns and challenges that need to be addressed when using ChatGPT in education. One of the main challenges is ensuring that the model is providing accurate and appropriate information to students. Additionally, there are concerns about the ethical and societal implications of using AI in education, such as the impact on jobs, privacy, and bias. The challenges for ChatGPT in education include: Data quality and bias: ChatGPT is trained on a large dataset of text, and any biases or inaccuracies in the data can be reflected in the model's output.
For the model to provide accurate and unbiased feedback, it is imperative to use a high-quality dataset that is representative of the question being posed to it. Interpreting and understanding the model's output: ChatGPT's output is generated based on patterns in the data it was trained on, and it is a challenge to understand how the model arrived at its output. There is no transparency to the mechanism involved in generation of results. This can make it challenging to understand the model's feedback and decide if it is accurate and relevant. Privacy and security: ChatGPT has access to sensitive student data, such as writing assignments, and it is important to ensure that this data is protected and used in compliance with applicable privacy laws. Another issue is that the ChatGPT could be used to cheat on exams or assignments, either by providing answers to test questions or by generating plagiarised content.
However, we must keep in mind that ChatGPT is "not an entity at all, but rather a complex algorithm generating meaningful sentences"  (Mollick, 2022, p. 5). As a result, this tool suffers from several limitations; the first one is that ChatGPT has learned everything it is in its database before 2021 and cannot, for the time being, search the internet to fill in information gaps. If ChatGPT's writing is clean, grammatically correct and well structured, it is often too vague and flat, and texts lack heart and soul  (Whitford, 2022). In addition, several students with the same inquiries may obtain very similar results. More worryingly, AI is very good at creating "convincing-sounding nonsense, devoid of truth"  (Mollick, 2022, p. 5). For example, ChatGPT struggles with confusingly worded questions, which can lead to incorrect answers.
In other words, it is a revolutionary AI chatbot technology that makes use of ML and NLP techniques to allow users to chat with a virtual assistant. It can provide a response for almost all tasks using written text. The usage of this application is currently open to the public for free as it is currently under the research and feedback gathering stage  (Ortiz, 2023a (Ortiz, , 2023b)). ChatGPT is a large language model. These models are trained with a very large dataset of conversational text such as chat logs, forums and social media posts to precisely forecast what word comes next in a given sentence which allows it to engage with the users in more realistic natural dialogues  (Montti, 2022; Qadir, 2022). The popularity of this technology can be imagined by the tweet from Open AI's chief Sam Altman who noted that ChatGPT attracted more than one million users in the first five days after its launch.
Future research should also work around developing models and validating them with relevant data on how to assess this technology's effectiveness, performance, and intentions for use by relevant stakeholders. Future researchers could further explore both positive (e. g. , engagement, attention, attitude, playfulness, hedonic and utilitarian motivation, excitement, willingness to us, etc. ) as well as the dark side (e. g. , threat, risk, invasion of security and privacy, escapism, distraction, response delay, irrelevant information, functional failure, etc. ) of the use of ChatGPT. In addition, this technology's capability to automatically assess student grades while providing relevant feedback and helping instructors periodically monitor students' progress should be explored. Moreover, ChatGPT's ability to generate text in multiple languages can also help researchers undertake research on assessing students' learning effectiveness and performance subjects in their own native language.
, 2020b; Watermeyer et al. , 2021). These wider narratives can also be seen in emerging national and international policy contexts (UNESCO, 2021; UK Government, 2021; Council of Europe, 2022), alongside a renewed focus globally on digital skills, computer science education, and associated national curriculum and qualifications reforms  (Brown et al. , 2014). The recent advancements in NLP have enabled the creation of accessible large language models, such as the open-source GPT (Generative Pre-trained Transformer) family created by OpenAI,  16  which has further reinvigorated interest in the use of AI tools and technologies in education. The widespread use and application of ChatGPT 17  (Chat Generative Pre-trained Transformer), which has been built on top of OpenAI's GPT-3 family of large language models, and has been finetuned for transfer learning using both supervised and reinforcement learning techniques  (OpenAI, 2022) , has the potential to transform the way we learn, teach, assess and access information.
One of the main ethical concerns is the accuracy and reliability of the information provided by ChatGPT, as well as the potential for bias and discrimination. As an AI language model, ChatGPT is trained on large corpora of textual data freely available on the internet, but it is not always possible to ensure that the information provided is accurate and up-to-date, and there is a risk that the model may perpetuate existing biases, stereotypes and discrimination in society. There have been extensive examples of racist, sexist, homophobic, ableist and other discriminatory language making its way into the model and is then generated as output. Large language models can reinforce and exacerbate existing power dynamics; the power structures reflected in the datasets become encoded in the models, meaning that any output reinforces those structures.
In addition to concerns regarding accuracy and bias, there are also more general concerns about the ethics and privacy implications of using ChatGPT in educational contexts, and what policies and regulations need to be in place to ensure that the technology is used securely, ethically and responsibly  (Irons & Crick, 2022). As mentioned previously, these types of AI models are reliant on huge amounts of data; some of this data is personal or private, and has been scraped indiscriminately along with other freely available data. Earlier models were shown to output private and identifying information from people in their datasets. Additionally, as models become more and more powerful there is an imperative to supply them with increasing volumes of data; this creates an economic imperative for the large language model creators to collect data in ways which are sometimes unethical or invade people's privacy.