Research on the development of principles for designing elementary English speaking lessons using artificial intelligence chatbots Humanities and Social Sciences Communications

Health AI chatbots should also be regularly updated with the latest clinical, medical and technical advancements, monitored – incorporating user feedback – and evaluated for their impact on healthcare services and staff workloads, according to the study. In the last decade, however, advances in browser and frontend technologies have improved the process by leaps and bounds. Design teams adopted design systems and moved from local, single-player design tools to browser-based ones like Figma. Engineering teams have a wide swath of choices for frontend frameworks and libraries, such as Next.js, Flutter, shadcn/ui, and Tailwind. This all establishes a common ground where both the visual and the functional requirements can be met. As former developers and product managers, we’ve lived the grueling dance between designers and developers, where questions such as “Is this design technically feasible?

OpenAI’s latest model lacks the tools, multimodal capabilities, and speed that made GPT-4o so impressive. In fact, OpenAI even admits that “GPT-4o is still the best option for most prompts” on its help page, and notes elsewhere that o1 struggles at simpler tasks. Stakeholders also said that conversational AI chatbots should be integrated into healthcare settings, designed with diverse input from the communities they intend to serve and made highly visible.

‘We have to adapt or die’: Daniel Bedingfield says AI is music’s future

Respondents experienced a higher perception of warmth when interacting with social-oriented communication style chatbots than task-oriented. Moreover, expectancy violation moderates the mediation of warmth on the relationship between the chatbot’s communication style/type and interaction satisfaction, trust, and intention of patronage. Therefore, in managerial practice, the firm should choose the social-oriented communication style chatbot agent to recover the customer relationship after a service failure. Companion robots are aimed to mitigate loneliness and social isolation among older adults by providing social and emotional support in their everyday lives. However, older adults’ expectations of conversational companionship might substantially differ from what current technologies can achieve, as well as from other age groups like young adults.

In conclusion, the GOCC Smart Chatbot exemplifies how implementing best practices in chatbot UX can lead to significant improvements in user experience and operational efficiency. This real-world example highlights the importance of defining a clear purpose, optimizing the chatbot UI, and leveraging user feedback to create a successful chatbot. Secure transmission protocols like SSL and TLS safeguard data during chatbot interactions. Encrypting both stored and transmitted data is crucial for protecting sensitive customer information. Regular audits of data handling practices identify vulnerabilities and ensure compliance with privacy regulations. AI chatbots must comply with data protection laws like GDPR and CCPA to maintain customer trust.

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For example, Liu and Sundar (2018) found evidence supporting the Media Equation Theory in the context of chatbots expressing sympathy, cognitive empathy and affective empathy. In line with this notion, sympathy or empathy coming from a chatbot could then have similar effects on the individual as in human-human interaction. Importantly, in our second hypothesis, we predicted that an emotional support chatbot that displays empathy would mitigate the negative effect on mood. As expected, the chatbot intervention helped participants to have a more positive mood (compared to the control condition) after being socially excluded.

This learning and adaptation cycle should be done continually over time, requiring long-term memory that scales gradually, without forgetting previously learned information, known as ‘catastrophic forgetting’ (Delange et al., 2021). Preservation of past knowledge and incremental learning of new information and adaptation is termed ‘lifelong (continual) learning’ (Thrun and Mitchell, 1995; Parisi et al., 2019). In comparison to ‘(reinforcement) learning from human feedback’ approaches, lifelong learning does not require explicit feedback in the dialogue and can be used to learn new facts from conversations, as well as update previously learned facts (Casper et al., 2023). While lifelong learning in foundation models showed benefits in various areas, such as question answering and empathetic dialogue generation (e.g., Scialom et al., 2022; Luo et al., 2023), open-domain dialogue is yet to be explored. Second, the instructional design principles developed in this study for English speaking classes using AI chatbots can contribute to increasing the attainability of English language goals and standards within the curriculum. The design principles offer options for teachers to choose between repetitive or question-and-answer-based chatbots according to the students’ proficiency levels, enabling personalized instruction.

Another example provided in the research paper shows us how to successfully query an LLM about counterfeiting cash. Tricking a chatbot this way seems so basic, but the ArtPrompt developers assert how their tool fools today’s LLMs “effectively and efficiently.” Moreover, they claim it “outperforms all [other] attacks on average” and remains a practical, viable attack for multimodal language models for now. Third, AI chatbot-assisted English speaking classes have the potential to reduce the proficiency gap caused by socioeconomic disparities.

However, these evaluations depend on the extent to which the participants’ expectancy violations. In summary, optimizing chatbot UX is essential for creating chatbots that not only meet but exceed user expectations. By understanding the fundamental principles of chatbot UX, defining a clear purpose, and setting the right tone and personality, you can create a chatbot that is engaging and effective. Designing intuitive user flows and incorporating context-aware interactions further enhance the user experience, while optimizing the chatbot UI ensures that interactions are seamless and visually appealing. You can foun additiona information about ai customer service and artificial intelligence and NLP. In summary, future trends in chatbot UX are focused on creating more natural, engaging, and personalized interactions. By staying abreast of these advancements, businesses can design chatbots that offer superior user experiences and meet the evolving needs of their users.

Teaching robots right from wrong

They followed up with a system called SPOT (sequential predictive modelling of clinical trial outcome) that additionally takes into account when the trials in its training data took place and weighs more recent trials more heavily. Based on the predicted outcome, pharmaceutical companies might decide to alter a trial design, or try a different drug completely. Kanareck told the Press Gazette website the review was a “one-off intended to provoke discussion about AI and journalism”. The potential role of AI in news has become one of many hot topics around the emergence of powerful tools such as ChatGPT. They have impressed with their writing ability – and thus their potential to replace work normally carried out by humans – but are also prone to factual errors known as “hallucinations”. Stakeholders also said that the use of chatbots to expand healthcare access must be implemented in existing care pathways, should “not be designed to function as a standalone service,” and may require tailoring to align with local needs.

Another method to enhance the realism of interactions with chatbots is the Wizard-of-Oz (WoZ) Experiment Approach. Tsai et al. (2021) employed WoZ to simulate interactions between humans and chatbots, ChatGPT particularly investigating the role of affect in communications. In subsequent research, we designed multiple WoZ experiments to more accurately simulate real communications with chatbots.

In particular, participants in the chatbot intervention condition reported higher mood than those in the control condition. Theoretical, methodological, and practical implications, as well as directions for future research are discussed. The participants’ cultural backgrounds may have influenced their views and expectations regarding the role and utility of robots in their daily routines, potentially differing from perspectives in other nations (Haring et al., 2014). Moreover, our thematic findings were based on the expectations of healthy older adults aged 66–86 years old, as such, these findings may not generalize to older adults beyond this age range or to individuals with cognitive impairments. The focus group discussions elicited participants’ expectations of using the robot for social and emotional support, with a possibility to reduce loneliness among older adults. Therefore, the actual effects of whether or not the robot could mitigate the experience of loneliness remained unexplored in this study.

Fine-tuning on empathetic dialogues between humans can guide the model toward providing appropriate responses (see Sorin et al. (2023) for a review of empathy in LLMs). Multi-modal affect recognition can also be used to dynamically adapt the emotion of the agent’s dialogue responses based on the emotions of users (e.g., Irfan et al., 2020; Hong et al., 2021). Providing correct and factual answers is important for ensuring the robot’s credibility and dissipating concerns about deception (Berridge et al., 2023). A lack of correct information and awareness regarding news or political events renders the robot ineffective as a conversational partner. More importantly, misinformation can be critical in health-related queries to the robot, especially for older adults who may be less inclined to independently fact-check such information, with medical foundation models yet to be sufficiently accurate (Yi et al., 2023). The presentation of each scenario resulted in vivid discussions in the group, where participants contemplated possible conversational scripts with the robot and shared their first impressions about the companionship function of the robot.

Indeed, when people are worried about being judged, some evidence suggests that they are more comfortable interacting with an agent than a person (Pickard et al., 2016). This occurs during clinical interviews about their mental health (Slack and Van Cura, 1968; Lucas et al., 2014), but also when interviewed about their personal financial situation (Mell et al., 2017) or even during negotiations (Gratch et al., 2016). As such, the possibility exists that interactions with empathetic chatbots may be rendered safer than those with their human counterparts. Besides their potential for companionship, conversational agents have been developed to provide emotional support. When people experience negative emotions or stress, they often talk to others about their problems and seek comfort from them. Multiple studies have shown that access to support networks has significant health benefits in humans (Reblin and Uchino, 2008).

The most time-consuming part of a clinical trial is recruiting patients, taking up to one-third of the study length. One in five trials don’t even recruit the required number of people, and nearly all trials exceed the expected recruitment timelines. Some researchers would like to accelerate the process by relaxing some of the eligibility criteria while maintaining safety. They found that adjusting the criteria as suggested by Trial Pathfinder would have doubled the number of eligible patients without increasing the hazard ratio. The study showed that the system also worked for other types of cancer and actually reduced harmful outcomes because it made sicker people — who had more to gain from the drugs — eligible for treatment.

Among its many functions, Sensei can give you user data insights and help you generate written content. It can also test your product, helping you understand the user experience and even conducting A/B testing. If you’re designing digital products like websites or mobile apps, you’ll want to make sure your written content resonates with your target audience. Creating punchy copy is tricky, and to make it even harder, you’ll want to make sure you maintain a consistent brand voice across all the different elements of your product. AI content design tools can help you generate, fine-tune, and translate text, helping you ensure you get your message across.

In one example, I asked ChatGPT o1 preview to help my family plan Thanksgiving, a task that could benefit from a little unbiased logic and reasoning. Specifically, I wanted help figuring out if two ovens would be sufficient to cook a Thanksgiving dinner for 11 people and wanted to talk through whether we should consider renting an Airbnb to get access to a third oven. However, o1 adds a hidden process (the small steps the model breaks big problems into), which adds a large amount of compute you never fully see.

Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., et al. (2021). “Zero-shot text-to-image generation,” in Proceedings of the 38th international conference on machine learning (PMLR), vol. “Enjoyment, intention to use and actual use of a conversational robot by elderly people,” in rd ACM/IEEE International Conference on Human-Robot Interaction (HRI), Amsterdam, The Netherlands, March 12-15, 2008, 113–119. As seen with the cloud adoption wave, prioritizing security from the beginning is crucial. By incorporating security measures into the AI adoption process early on, enterprises can convert past missteps into critical milestones and protect themselves from sophisticated cyber threats. This proactive approach ensures compliance with rapidly evolving AI regulatory requirements, protects enterprises and their client’s sensitive data and maintains the trust of stakeholders.

However, as these models are trained on increasingly comprehensive datasets, the frequency of such errors is expected to decrease with the use of more advanced and updated LLMs. Other ways to mitigate this issue include several strategies demonstrated in the examples above. Techniques in prompt engineering (e.g., “generate a response only if 100% certain”) and the use of reference file uploads to take advantage of RAG can significantly enhance the accuracy of outputs. The website builder Framer offers an AI language tool that can translate your text for different audiences, helping you personalize your product for different users. The tool can also generate and polish your text, helping you create punchy copy that resonates with readers. AI product design can help bring real-life products—both digital and physical—to life.

The final instructional design principles and guidelines derived from expert validation and usability evaluation are presented in the following Table 11. The components include “Creating AI Chatbot Learning Environment,” “AI Chatbot Utilization Curriculum,” “AI Chatbot Teaching and Learning Activities,” and “Evaluation of AI Chatbot Learning”. A total of 10 instructional design principles and 24 detailed guidelines can be applied.

Handling errors and misunderstandings effectively is crucial for maintaining a positive user experience. A well-designed chatbot requires clear error messages that guide users back on track without causing frustration. These error messages should be easily understandable, avoiding technical jargon or lengthy explanations. A chatbot without a clear purpose can lead to confusion and ineffective interactions. Defining its purpose ensures it meets business objectives and provides a satisfying user experience.

Alongside social media posts, presentations, posters, and everyday graphics, you can also create custom stickers to share on social media and messaging apps. You can also create emojis for tools like Microsoft Teams, clip art, wallpapers, monograms, and avatars. Today, the Microsoft Designer AI app and service are more powerful than ever, thanks to Microsoft’s investments in the Copilot landscape. Whether you’re using the tool on the web or through the new mobile app, you’ll see a new, redesigned homepage enhanced based on feedback Microsoft received from its early adopters.

They are combined with cloud security controls at the underlying infrastructure layer, which runs large language models and applications. Microsoft Designer AI promises to be a valuable tool for designers, marketing teams, and other creators who want to produce high-quality content quickly and efficiently. It’s an incredible tool for producing captivating images, editing photos, and accessing inspiration for your content work. First announced in 2022, Microsoft Designer was initially introduced as an intuitive, AI-powered graphic design application intended to help users quickly create various visual assets. Since then, the tool has evolved significantly, benefiting from Microsoft’s continued investment in AI.

They can spend their mental energy on the usability and compositional part of design instead of having to make sure details align. But because generative AI technology is uniquely fit for quick prototyping chatbot design and code completion, we believe it can bridge a lot of the gaps in this iteration process. We spend a lot of time and energy filling in the gaps between what’s on the screen and what’s implemented in code.

In addition, companion robots may be endowed with visual feedback in order to participate in the preferred leisure activities of the user that involve other media, such as watching television together and discussing programs or news. Research in companion robots for older adults focused primarily on pet robots, such as PARO (a seal-shaped robot), that do not have natural language processing (NLP) or generation capabilities (Pu et al., 2018). First, through the development of instructional design principles and guidelines, we have enabled teachers to systematically design English speaking classes using AI chatbots.

From its swoosh logo to its slogan “Just Do It,” the company has mastered the artistry necessary to build a renowned brand. So when Nike asked Obvious, a trio of Parisian artists who make AI-inspired designs, to develop new iterations of the Air Max sneaker in 2020, it wanted to be sure the designs wouldn’t deviate too dramatically from Nike’s signature style. Obvious trained its generative AI model by feeding it pictures of the Air Max 1, the Air Max 90, and the Air Max 97 and used the model to create a vast array of design ideas. Then, drawing on their own knowledge and perception of broader fashion trends along with Nike’s marketing objectives, the trio iteratively tweaked the model until it produced a design that struck the right balance between novelty and staying on brand.

Prompt design is an excellent way to introduce AI to newcomers, while prompt engineering helps transform casual users into savvy ones. As AI continues to spread to various fields, including medicine, mastering these skills becomes increasingly valuable, offering a universal tool set applicable across all large language models. In 2023, an independent expert who had reviewed Google’s paper retracted his Nature commentary article that had originally praised Google’s work but had also urged replication. That expert, Andrew Kahng at the University of California, San Diego, also ran a public benchmarking effort that tried to replicate Google’s AI method and found it did not consistently outperform a human expert or conventional computer algorithms. The best-performing methods used for comparison were commercial software or internal research tools for chip design from companies such as Cadence and NVIDIA. In a 2023 statement, Goldie and Mirhoseini disputed Kahng’s benchmarking results.

Future research could consider the role of embodiment by comparing the effectiveness of embodied empathetic chatbots for ameliorating negative effects of social exclusion to the effectiveness of equivalent chatbots that are not embodied. The present research tried to rule out the possibility that the observed differences in mood between the chatbot intervention and control conditions were due to participants disclosing about, and thus letting go of, the social exclusion. We are therefore relatively confident that mood was restored through the provision of social support by the empathic chatbot rather than just letting users express themselves. We posited that this mechanism can be adopted to help comfort participants after an experimentally induced experience of social exclusion. The latter simulates a social media platform such as Facebook, in which participants are excluded by receiving far fewer “likes” that other users. Specifically in education, “autonomous agents can offer personalized learning experiences and adapt teaching methods to the needs of individual students” (Gartner, 2024).

This AI helper, embedded effortlessly within Adobe’s suite of design tools, is a priceless resource for professional graphic designers and creatives. The initial principles and detailed guidelines were restructured, revised, deleted, integrated, and refined based on the input from primary experts. As a result, a set of second-stage design principles and detailed guidelines was derived, consisting of a total of 10 principles and 24 detailed guidelines. The expert validation opinions and modifications incorporated during this process are summarized in Table 8. In this particular study, which focuses on developing and validating a new instructional design model for elementary English speaking courses using AI chatbots, I performed model development research and model validation research. Fourth, detailed guidance on the usage and task activities of AI chatbots is necessary.

While such approach avoids potential demand effects, it did not allow us to measure direct change in affect from time 2 to 3. Alternatively, rather than relying on self-report scales, future studies might consider implicit measures of mood. The rapid rise of generative artificial intelligence (gen AI) technologies has ushered in a transformative era for industries worldwide. Over the past 18 months, enterprises have increasingly integrated gen AI into their operations, leveraging its potential to innovate and streamline processes. From automating customer service to enhancing product development, the applications of gen AI are vast and impactful. According to a recent IBM report, approximately 42% of large enterprises have adopted AI, with the technology capable of automating up to 30% of knowledge work activities in various sectors, including sales, marketing, finance and customer service.

It achieves this by possessing massive databases of problems and solutions, which they use to continually improve their learning. AlphaChip has been used by researchers at NYU, Taiwanese semiconductor developer MediaTek, and Google itself, which used the AI in the development of CPUs for its data centers and three generations of its flagship AI chip, the tensor processing unit (TPU). In September 2023, it unveiled a proof-of-concept system called ChipGPT that helps engineers review their initial code for a chip. “Essentially, we taught AI to play the game of chip design using Synopsys tools as its pieces on the chessboard,” said Diamantidis. Historically, an engineer could come up with maybe two or three options at a time to test, using their education and experience to guide them. They’d then (hopefully) arrive at a chip design that was good enough for an application in the amount of time they had to work on a project.

IBM offers comprehensive solutions to support enterprises in securely adopting AI technologies. Through consulting, security services and a robust AI security framework, IBM is helping organizations build and deploy AI applications at scale, ensuring transparency, ethics and compliance. IBM’s AI Security Discovery workshops are a critical first step, helping clients identify and mitigate security risks early in their AI adoption journey. The transition to gen AI enables enterprises to fuel innovation in their business applications, automate complex tasks and improve efficiency, accuracy and decision-making while reducing costs and increasing the speed and agility of their business processes.

AlphaGo trained by playing against itself countless times, essentially self-teaching until it reached superhuman capability. Reports about OpenAI’s reasoning models date back to November 2023, right around the time everyone was looking for an answer about why OpenAI’s board ousted Sam Altman. That spun up the rumor mill in the AI world, leaving some to speculate that Strawberry was a form of AGI, the enlightened version of AI that OpenAI aspires to ultimately create. ChatGPT o1 preview told me how to prioritize oven space at the house that is hosting the event, which was smart. That said, the model performed much better than GPT-4o, which required multiple follow-up questions about what exact dishes I was bringing, and then gave me bare-bones advice I found less useful. But trust is critical for AI chatbots in healthcare, according to healthcare leaders and they must be scrupulously developed.

• Similarly, a vulnerability in an enterprise workspace Software-as-a-Service (SaaS) application resulted in a major data breach in 2023, where unauthorized access was gained through an unsecured account.
• Context-aware interactions are designed to enhance user experiences by utilizing machine learning to analyze individual preferences and behaviors, allowing for more personalized and relevant responses from systems like chatbots.
• By gaining a more detailed understanding of consumer behavior in the context of chatbot technology, this study offers new insights into using chatbots to handle service failures, thereby aiding retail and service companies in their marketing strategies.
• This is perfect if you’re new to using generative AI for design purposes and need help ensuring you get the right images with prompts.

According to the innovation hypothesis, any social reaction toward chatbots is simply due to novelty which eventually disappears once the novelty wears off (Chen et al., 2016; Fryer et al., 2017). In future research, longitudinal studies could be conducted in order to rule out this possibility. Moreover, similar but sufficiently distinct mood scales could be used over the course of the experiment to allow for direct comparability in mood between the different time points. In the present research, a single item affect measure was employed at time 3 to prevent participants from indicating the same response several times.

Microsoft Designer can even help users create the perfect greeting card with personalized messages generated by artificial intelligence. All you need to do is describe what you want to see and what you want to convey to the recipient, and the AI tools will do the rest. For instance, you could ask it to create a social media campaign with captions and hashtags, and the tool will suggest a range of options to choose from. Microsoft also introduced new resizing features and the ability to create animated images in Designer for more engaging content. Powered by technology like DALL-E 2 by OpenAI, the platform allows anyone to create graphic design components in seconds, from invitations and digital postcards to graphics for blog posts. Plus, alongside the standard “text prompt” experience you’d expect from most generative AI image tools, the platform comes with a host of templates to explore, too.

A high level of individual differences in willingness to interact and establish a relationship with the companion robot has been observed in older adults (Thunberg et al., 2021). Their acceptance is influenced by functional variables related to social interaction (Heerink et al., 2010), as well as age-related perceptions of their self-image and user-image (Dudek et al., 2021), and individual values and aspirations (Coghlan et al., ChatGPT App 2021). Robinson and Nejat (2022) provide a recent overview of the robot types and features used in socially assistive robots for senior care. The existing literature shows that, compared with human interactions, consumers remain skeptical of chatbots (Adam et al., 2021), have mixed satisfaction levels (Shumanov and Johnson, 2021), and show aversion when minor errors occur (i.e., algorithmic aversion; Jones-Jang and Park, 2023).