30 Sep How to use generative AI in tailored student engagement

ChatGPT is going to change education, not destroy it

That’s the part of the system leaders are referring to when they say it can “accelerate” learning. The chatbot can also help students and parents who don’t speak English as their first language by translating information it displays into about 100 different languages, says Smith-Griffin. They found they enabled open-domain conversational capabilities, including generalizing scenarios not seen in training and reducing knowledge hallucination in advanced chatbots. Equally, Bang et al. (2023) find that ChatGPT has 63.41% accuracy on average in 10 different reasoning categories under logical reasoning, non-textual reasoning, and commonsense reasoning, which makes it an unreliable reasoner. Patel and Lam (2023) discuss the potential use of ChatGPT, an AI-powered chatbot, for generating discharge summaries in healthcare. They report that ChatGPT allows doctors to input specific information and develop a formal discharge summary in seconds.

Focusing on teaching and learning, Kohnke et al. (2023) analyze ChatGPT’s use in language teaching and learning in their study. The researchers look into the advantages of using ChatGPT, a generative AI chatbot, in language learning. As a final point, the study emphasizes the crucial digital skills that instructors and students must have to use this chatbot to improve language learning in an ethical and efficient manner. Another study was undertaken by Baidoo-Anu and Owusu Ansah (2023) to examine ChatGPT’s potential for facilitating teaching and learning.

Several academic articles also support using ML algorithms to detect cheating by analyzing student data. Some examples include Kamalov et al. (2021), who propose an ML approach to detect instances of student cheating based on recurrent neural networks combined with anomaly detection algorithms and find remarkable accuracy in identifying cases of student cheating. Similarly, Ruipérez-Valiente et al. (2017) employed an ML approach to detect academic fraud by devising an algorithm to tag copied answers from multiple online sources. Their results indicated high detection rates (sensitivity and specificity measures of 0.966 and 0.996, respectively).

Moreover, ChatGPT’s extensive knowledge base allows it to quickly generate accurate and relevant information. This accessibility to a wide range of knowledge empowers students to explore diverse perspectives and engage in critical thinking. ChatGPT supports students in understanding complex concepts by providing comprehensive and up-to-date information, thereby improving their learning outcomes. After the pandemic, city, district, and community leaders sounded the alarm about the need to provide more support to improve student achievement. Student performance in math and English language arts on spring state test scores in 2023 went up by 2 percentage points from the prior year, highlighting slow academic recovery after the pandemic. That required more than 10,000 public school students to attend summer school in 2023 – double the number from the year before.

Neuromorphic computing hub at University of Texas at San Antonio to be largest in U.S.

As an example, think of a prospective student exploring a university’s website. Instead of passively browsing, they are greeted by a chatbot that answers their questions. The chatbot also offers comprehensive support for every aspect of student life, guiding students through the application process, tailoring financial aid options to their needs and providing easy access to campus resources like technology or tutoring services. This tailored experience makes the student feel understood and connected, increasing their likelihood of success. Generative AI chatbots are redefining the concept of student support by anticipating needs before they are even voiced.

The use of biometric verification for cheating prevention is also backed by research. Rodchua et al. (2011) review biometric systems, like fingerprint and facial recognition, to ensure assessment integrity in HEIs. Similarly, Agulla et al. (2008) address the lack of face recognition in learning management systems and propose a FaceTracking application using webcam video. Agarwal et al. (2022) recommend an ML-based keystroke biometric system for detecting academic dishonesty, reporting 98.4% accuracy and a 1.6% false-positive rate. ChatGPT4 was launched on March 14, 2023, and provides makers, developers, and creators with a powerful tool to generate labels, classify visible features, and analyze images.

Introducing ChatGPT Edu of OpenAI

Western Governors University, or WGU, operates completely online, meaning it has no actual grounds or physical campus. In 2018, the school’s non-profit research agency, WGU Labs, received a $750,000 award from the National Science Foundation to experiment with a new kind of chatbot. But the reality is, a lot of the work that they do and continued interaction with the institution … takes place outside of the classroom, online … day and night,” Rajecki told VOA. But most importantly, he said, students want support outside of the hours when it is normally available. Having human beings available to answer people’s questions and complaints can be costly, requiring many workers. And in most cases, employees can only work a set number of hours in a day, increasing the amount of time customers wait for a response.

• In conclusion, the use of ChatGPT in education has the potential to influence student engagement and learning outcomes positively.
• Rowan College at Burlington County in New Jersey addressed this need through a mid-semester check-in campaign, using a simple text message asking students to reply with an emoji to indicate how they were feeling.
• Furthermore, another concern is that GDPR and the UK Data Protection Act 2018 (DPA) provide individuals with the ‘right to be informed’ about how their data is processed; however, overall algorithmic transparency is low (Meyer von Wolff et al., 2020).
• However, there are also acknowledged drawbacks, such as the potential for producing inaccurate information, biases in data training, and privacy issues.
• Click the banner to remind yourself of the good things AI can do for the student experience.

AI for Education’s Bickerstaff said developers “have to take caution” when building these systems for schools, especially those like Ed that bring together such large sets of data under one application. And she adds that the idea is to use algorithms to make personalized recommendations to each student about what will help his or her learning — the way that Netflix recommends movies based on what a user has watched in the past. The student can click on the activities, which show up in a window that automatically opens, say, a math assignment in IXL, an online system used at many schools. The tasks Ed surfaces are pulled from the learning management system and other tools that his school is using, and Ed knows what assignments Alberto has due for the next day and what other optional exercises fit his lessons.

Computer self-efficacy has received much attention in prior studies (Compeau and Higgins, 1995; Teo and Koh, 2010), but few studies have researched AI self-efficacy. Chatbots can leverage natural language processing (NLP), an AI subfield that enables machines to understand, respond to, and generate human language. Previously, chatbots’ primary function was simply to mimic human conversation, whereas platforms such as ChatGPT have abilities that far extend that.

Every Wednesday and Friday morning, Chalkbeat Newark will send you only the most important news about the city’s public schools and statewide education policy. So far, there have been errors in how Khanmigo solves basic math problems, which Kunz said they have since fixed. Teachers and students across pilot districts have also said the tool occasionally offers too much help and was too available, especially when students were taking assessments such as quizzes and course challenges, Kunz said. Khanmigo is still in its pilot phase but is designed to guide students as they progress through lessons and ask questions like a human tutor would, according to Khan Academy spokesperson Barb Kunz.

Additionally, Chavez et al. (2023) suggest a neural network approach to forecast student outcomes without relying on personal data like course attempts, average evaluations, pass rates, or virtual resource utilization. Their method attains 93.81% accuracy, 94.15% precision, 95.13% recall, and 94.64% F1-score, enhancing the educational quality and reducing dropout and underperformance. Likewise, Kasepalu et al. (2022) find that an AI assistant can help teachers raise awareness and provide ChatGPT a data bank of coregulation interventions, likely leading to improved collaboration and self-regulation. An open letter of +50 K signatories emphasizes the need for robust AI governance systems, such as new regulatory authorities, tracking systems, auditing and certification, and liability for AI-caused harm. Finally, they suggest that a pause on AI development is necessary to ensure it is used for the benefit of all and to give society a chance to adapt (Bengio et al., 2020).

Artificial Intelligence Holds Promise for Education — and Creates Problems

Regular monitoring and evaluation of the use of ChatGPT should be conducted to assess its effectiveness and address any ethical concerns that may arise. This monitoring can involve reviewing the interactions between students and the AI chatbot, analyzing the quality and accuracy of the generated content, and gathering feedback from both students and teachers. By actively monitoring its performance, institutions can identify and address issues, refine the system, and enhance the overall user experience. Encouraging critical thinking and evaluation skills among students is crucial when utilizing ChatGPT in an educational context. Students should be taught to approach the information generated by the AI chatbot with a discerning mindset, questioning and verifying its accuracy through independent research and analysis. This empowers them to develop critical thinking skills and avoid mindlessly accepting information provided by AI systems.

Similar to other transformative technologies, such as social media in the classroom, using AI comes with striking a reasonable balance of the benefits and shortfalls. As Williams (2022) argues in their study of social media and pedagogy, AI has the potential to both enhance and disrupt learning. Therefore, it is important to use AI in a way that maximises its benefits for practitioners and students, while minimising its risks relating to ethics and safeguarding. This will likely involve setting firm ethical boundaries to safeguard the interests of students, educators, and the broader educational community. The author argues that oral presentations, such as viva voices and group projects, could be an effective assessment method to discourage plagiarism and promote learning outcomes.

Can chatbots improve medical education?

However, additional examples would include studies analyzing the influence of AI chatbots among university students experiencing symptoms of depression and anxiety (Fitzpatrick et al., 2017; Fulmer et al., 2018; Klos et al., 2021). Similarly, Bendig et al. (2019) develop a comprehensive literature review on using chatbots in clinical psychology and psychotherapy research, including studies employing chatbots to foster mental health. Adopting AI chatbots like ChatGPT in HEIs can positively affect various academic activities, including admissions, as they can streamline enrollment with tailored approaches to individual student needs. Student services can also benefit from AI chatbots, as they can provide personalized assistance with financing, scheduling, and guidance. Additionally, AI chatbots can enhance teaching by creating interactive learning experiences to assist students in comprehending course material, providing personal feedback, and aiding researchers in data collection and analysis.

The document notes that Ed also interfaces with the Whole Child Integrated Data stored on Snowflake, a cloud storage company. Launched in 2019, the Whole Child platform serves as a central repository for LAUSD student data designed to streamline data analysis to help educators monitor students’ progress and personalize instruction. The integration of ChatGPT in teaching and learning can significantly impact educators’ roles and the entire teaching-learning process. ChatGPT can revolutionize traditional instructional practices with its interactive and conversational capabilities and open new possibilities for personalized and engaging learning experiences.

Subsequently, this may lead to an overprotective reaction to a potential opportunity, such as New York City’s schools’ banning of ChatGPT from educational networks due to the risk of using it to cheat on assignments (Shen-Berro, 2023). Contrastingly, there may be a naïve acceptance of AI in education as ‘the one’ technology that will fundamentally change education provision and practice, overlooking and repeating high-profile failures of TEL in the past (Oppenheimer, 1997). However, the value of chatbots extends beyond saving time on administrative burdens; rather, they can additionally transform pedagogy (Watermeyer et al., 2023). For instance, an educator may use chatbots to generate case studies for a seminar or provide best practices relating to academic skills. Microsoft (2023) describe AI as the ability of a computer system to mimic human cognitive functions such as learning and problem-solving.

Nevertheless, concerns surrounding the accuracy and integrity of AI-generated scientific writing underscore the need for robust fact-checking and verification processes to uphold academic credibility. The reliance on AI-generated content in scientific literature raises questions about the potential for misinformation and the need to establish mechanisms for transparently identifying and attributing AI-generated contributions in academic publications. Researchers and publishers must work together to ensure rigorous standards for fact-checking and validation when incorporating AI-generated content into scientific papers, safeguarding the quality and reliability of scholarly work (Alkaissi and McFarlane, 2023). Moreover, the paper delves into the critical investigation of using ChatGPT to detect implicit hateful speech. By employing this AI language model to elicit natural language explanations, researchers evaluate its proficiency and compare responses with human-labeled data—shedding light on its potential contributions to address societal issues like hate speech online.

The rise of LLMs could also widen the “AI divide” between those with access to the most powerful AI systems and those without, potentially amplifying existing societal divides and inequalities. To gauge the media impact since the launch of ChatGPT on Nov. 30, 2022, we compared Google user search interests using Google Trends. This web service displays the search volume of queries over time in charts across countries and languages – Figure 1 shows ChatGPT’s overwhelming media impact since its November 30, 2022 launch. The data depicted in the chart is in line with Libert (2023) findings, which show that the search interest for ChatGPT soared to 112,740%. The chatbot is designed to work across multiple devices and platforms, allowing students to access it easily, whether on campus or studying remotely. Instead of switching to in-class examination to prohibit the use of AI (which some may be tempted to do), educators can design assessments that focus on what students need to know to be successful in the future.

This process seems simple but in practice is complex and works the same whether the chatbot is voice- or text-based. Despite landing millions of dollars in backing from a group of social impact investment firms, several of which cited their enthusiasm for investing in AllHere specifically because it was led by a Black woman, court records reveal the company’s coffers are nearly empty. AllHere claimed nearly $2.9 million in property and just shy of that — $1.75 million — in liabilities. The company’s actual assets, Toby Jackson acknowledged in court, are much lower.

Previous research on technology adoption has demonstrated that the higher a person’s perceived self-efficacy regarding a particular application, the higher the perceived usefulness of that application (Igbaria, 1995; John, 2013; Lee and Ryu, 2013). However, since perceived self-efficacy is a highly domain-specific construct, more than perceptions of general self-efficacy measures may be required to cover the scope of AI adoption (Bandura, 2006). Chatbots’ ability to promote autonomy in learning holds substantial promise for personalised, student-centred education.

This issue is particularly paramount in educational ecosystems that emphasise outcomes or end goals, such as grades or qualifications, over the learning process. For example, all phases of the UK’s education systems have traditionally emphasised these quantifiable measures of academic success (Mansell, 2007). In response to the first RQ, it aims to explore the positive impacts of ChatGPT in education, focusing on enhanced learning and improved information access. It also addresses challenges, including biases in AI models, accuracy issues, emotional intelligence, critical thinking limitations, and ethical concerns. The goal is to identify methods to enhance ChatGPT’s performance while promoting ethical and responsible use in educational settings. Concerning the use of AI chatbots to retain students, earlier articles highlight the advantages these chatbots offer, potentially improving student retention.

Frequent encounters with AI hallucinations can decrease students’ trust in AI as a reliable educational tool, and this distrust can extend to other digital learning resources and databases. Educators, policymakers, and AI developers must recognise these potential biases and take proactive steps to mitigate them. Firstly, the datasets used to train these AI systems should be diverse and representative to avoid amplifying societal biases. Nazer et al. (2023) argue that the issue stems from chatbots using data from either a single or narrow source, thus, propose that to ensure the data is truly representative, educational institutes should partner to share data.

Reis-Marques et al. (2021) analyzed 61 articles on blockchain in HEIs, including several addressing educational fraud prevention. Tsai and Wu (2022) propose a blockchain-based grading system that records results and activities, preventing post-grade fraud. Islam et al. (2018) suggest a two-phase timestamp encryption technique for question sharing chatbot for educational institutions on a blockchain, reducing the risk of exam paper leaks and maintaining assessment integrity. The use of predictive analytics to detect academic fraud is also supported by academic research. Indeed, Trezise et al. (2019) confirm that keystroke and clickstream data can distinguish between authentically written pieces and plagiarized essays.

For instance, a student might use a chatbot to assist them in a burdensome administrative task like filling out an ethics form. Unsurprisingly, AI could potentially identify more ethical risks for a research project related to data protection, ChatGPT App confidentiality, and anonymity in a research project than a student might. Using AI to support a risk assessment may be useful, but there is certainly value in the student being able to identify and manage the ethical risks themselves.

However, the emergence of ChatGPT and similar technologies may require regulatory frameworks to address privacy, security, and bias concerns, ensuring accountability and fairness in AI-based services. Rules must not impede AI-based tech development, as uncertainty can threaten investments. The US commerce department is creating accountability measures for AI tools (Bhuiyan, 2023), soliciting public feedback on assessing performance, safety, effectiveness, and bias, preventing misinformation, and ensuring privacy while fostering trustworthy AI systems. The reasons for humans to fear the development of AI chatbots like ChatGPT are many and compelling, although it is too early to support such fears with solid statistical evidence. Therefore, when writing this article, only partial and anecdotal evidence can be presented. Indeed, according to a report by researchers at Stanford University (AI Index Steering Committee, 2023), 36% of experts believe that decisions made by AI could lead to “nuclear-level catastrophes” (AI Index Steering Committee, 2023, p. 337).

The process of building a chatbot can seem daunting, and it is a time- and resource-intensive project, but the benefits outweigh the risks of using a prebuilt, third-party option. CDW has the experience and expertise to ensure data stays segregated and stowed far enough away from the AI that requests for personal data won’t be answered — at least, not without another layer of security on top. You can foun additiona information about ai customer service and artificial intelligence and NLP. When configuring a chatbot’s access permissions, it’s useful to remember that there’s nothing about chatbots that makes them immune to the data privacy challenges plaguing the rest of the internet. From the very beginning, when a chatbot is being trained on real-world examples to build its neural base, to the moment when it is released on the world and uses new queries from users to continue its learning, data is being ingested. But lost in some of the clamor over generative AI tools like ChatGPT is the reality that AI has been a helpful ally to colleges and universities for years.

Addressing biases requires careful data curation, identification, and mitigation techniques to ensure fairness and inclusivity in the AI model’s responses. In today’s rapidly evolving educational landscape, imagine a prospective student or single parent  searching for information about financial aid options at midnight. With no one available to answer their questions, they may feel frustrated and uncertain. AI-based learning experiences must recognize that AI technologies are trained using existing data and are ill-equipped to tackle novel problems without training data.

After analyzing the ethical considerations discussed within the selected articles, the results are shown in the following tables. These tables provide an alternative representation of the ethical considerations and safeguards discussed in the paragraph. Table 4 focuses on ethical considerations, such as clear guidelines, human supervision, training, critical thinking, and privacy.