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Customer care is important for its role in relationship building. This role has traditionally been performed by human customer agents; however, the emergence of interactive generative AI (GenAI) shows potential for using AI for customer care in emotionally charged interactions. Bridging practice and the academic literatures in marketing and computer science, this article develops an AI-enabled customer care journey, from accurate emotion recognition to empathetic response, emotional management support, and, finally, the establishment of an emotional connection. Marketing requirements for each of the stages are derived from in-depth interviews with top managers and a survey of chief marketing officers. By juxtaposing these requirements against the current feeling capabilities of GenAI, the authors highlight the technological challenges engineers must tackle. The article concludes with a set of marketing tenets for implementing and researching the caring machine. These include verifying emotion recognition accuracy using marketing emotion theories through multiple emotion signals and methods, utilizing prompt engineering to enhance GenAI's emotion understanding, employing "response engineering" to personalize emotion management recommendations, and strategically deploying GenAI for emotional connection to simultaneously enhance customer emotional well-being and customer lifetime value. [ABSTRACT FROM AUTHOR]

This research examines the transformative potential of artificial intelligence (AI) in general and Generative AI (GAI) in particular in supply chain and operations management (SCOM). Through the lens of the resource-based view and based on key AI capabilities such as learning, perception, prediction, interaction, adaptation, and reasoning, we explore how AI and GAI can impact 13 distinct SCOM decision-making areas. These areas include but are not limited to demand forecasting, inventory management, supply chain design, and risk management. With its outcomes, this study provides a comprehensive understanding of AI and GAI's functionality and applications in the SCOM context, offering a practical framework for both practitioners and researchers. The proposed framework systematically identifies where and how AI and GAI can be applied in SCOM, focussing on decision-making enhancement, process optimisation, investment prioritisation, and skills development. Managers can use it as a guidance to evaluate their operational processes and identify areas where AI and GAI can deliver improved efficiency, accuracy, resilience, and overall effectiveness. The research underscores that AI and GAI, with their multifaceted capabilities and applications, open a revolutionary potential and substantial implications for future SCOM practices, innovations, and research. [ABSTRACT FROM AUTHOR]

ChatGPT and generative artificial intelligence (Gen-AI) are transforming firms and supply chains. However, the empirical literature reporting the benefits, challenges, and outlook of these nascent technologies in operations and supply chain management (OSCM) is limited. This study surveys current projects and perceptions of these technologies in US (n = 119) and UK (n = 181) supply chains. We found that projects range from proof-of-concept to full implementation, with a main focus on operational gains, such as improved customer satisfaction, cost minimisation, and process efficiencies. The main challenges concern data, technological and organisational issues. Expected benefits are dominated by cost savings and enhanced customer experience, but also include increased automation and sustainability. Industries were found to cluster around six groups according to perceived benefits and implementation challenges. Our findings contribute to the emerging literature on Gen-AI use in OSCM, and to management practice by mapping the benefits, challenges, outlook, and maturity level of Gen-AI projects in supply chains. [ABSTRACT FROM AUTHOR]

Previous research has shown that consumers respond differently to decisions made by humans versus algorithms. Many tasks, however, are not performed by humans anymore but entirely by algorithms. In fact, consumers increasingly encounter algorithm-controlled products, such as robotic vacuum cleaners or smart refrigerators, which are steered by different types of algorithms. Building on insights from computer science and consumer research on algorithm perception, this research investigates how consumers respond to different types of algorithms within these products. This research compares high-adaptivity algorithms, which can learn and adapt, versus low-adaptivity algorithms, which are entirely pre-programmed, and explore their impact on consumers' product preferences. Six empirical studies show that, in general, consumers prefer products with high-adaptivity algorithms. However, this preference depends on the desired level of product outcome range—the number of solutions a product is expected to provide within a task or across tasks. The findings also demonstrate that perceived algorithm creativity and predictability drive the observed effects. This research highlights the distinctive role of algorithm types in the perception of consumer goods and reveals the consequences of unveiling the mind of the machine to consumers. [ABSTRACT FROM AUTHOR]

BACKGROUND: Qualitative methods are incredibly beneficial to the dissemination and implementation of new digital health interventions; however, these methods can be time intensive and slow down dissemination when timely knowledge from the data sources is needed in ever-changing health systems. Recent advancements in generative artificial intelligence (GenAI) and their underlying large language models (LLMs) may provide a promising opportunity to expedite the qualitative analysis of textual data, but their efficacy and reliability remain unknown. OBJECTIVE: The primary objectives of our study were to evaluate the consistency in themes, reliability of coding, and time needed for inductive and deductive thematic analyses between GenAI (ie, ChatGPT and Bard) and human coders. METHODS: The qualitative data for this study consisted of 40 brief SMS text message reminder prompts used in a digital health intervention for promoting antiretroviral medication adherence among people with HIV who use methamphetamine. Inductive and deductive thematic analyses of these SMS text messages were conducted by 2 independent teams of human coders. An independent human analyst conducted analyses following both approaches using ChatGPT and Bard. The consistency in themes (or the extent to which the themes were the same) and reliability (or agreement in coding of themes) between methods were compared. RESULTS: The themes generated by GenAI (both ChatGPT and Bard) were consistent with 71% (5/7) of the themes identified by human analysts following inductive thematic analysis. The consistency in themes was lower between humans and GenAI following a deductive thematic analysis procedure (ChatGPT: 6/12, 50%; Bard: 7/12, 58%). The percentage agreement (or intercoder reliability) for these congruent themes between human coders and GenAI ranged from fair to moderate (ChatGPT, inductive: 31/66, 47%; ChatGPT, deductive: 22/59, 37%; Bard, inductive: 20/54, 37%; Bard, deductive: 21/58, 36%). In general, ChatGPT and Bard performed similarly to each other across both types of qualitative analyses in terms of consistency of themes (inductive: 6/6, 100%; deductive: 5/6, 83%) and reliability of coding (inductive: 23/62, 37%; deductive: 22/47, 47%). On average, GenAI required significantly less overall time than human coders when conducting qualitative analysis (20, SD 3.5 min vs 567, SD 106.5 min). CONCLUSIONS: The promising consistency in the themes generated by human coders and GenAI suggests that these technologies hold promise in reducing the resource intensiveness of qualitative thematic analysis; however, the relatively lower reliability in coding between them suggests that hybrid approaches are necessary. Human coders appeared to be better than GenAI at identifying nuanced and interpretative themes. Future studies should consider how these powerful technologies can be best used in collaboration with human coders to improve the efficiency of qualitative research in hybrid approaches while also mitigating potential ethical risks that they may pose.

Multiscale design of catalyst layers (CLs) is important to advancing hydrogen electrochemical conversion devices toward commercialized deployment, which has nevertheless been greatly hampered by the complex interplay among multiscale CL components, high synthesis cost and vast design space. We lack rational design and optimization techniques that can accurately reflect the nanostructure-performance relationship and cost-effectively search the design space. Here, we fill this gap with a deep generative artificial intelligence (AI) framework, GLIDER, that integrates recent generative AI, data-driven surrogate techniques and collective intelligence to efficiently search the optimal CL nanostructures driven by their electrochemical performance. GLIDER achieves realistic multiscale CL digital generation by leveraging the dimensionality-reduction ability of quantized vector-variational autoencoder. The powerful generative capability of GLIDER allows the efficient search of the optimal design parameters for the Pt-carbon-ionomer nanostructures of CLs. We also demonstrate that GLIDER is transferable to other fuel cell electrode microstructure generation, e.g., fibrous gas diffusion layers and solid oxide fuel cell anode. GLIDER is of potential as a digital tool for the design and optimization of broad electrochemical energy devices.

Our research strives to examine how simulation models of logistics systems can be produced automatically from verbal descriptions in natural language and how human experts and artificial intelligence (AI)-based systems can collaborate in the domain of simulation modelling. We demonstrate that a framework constructed upon the refined GPT-3 Codex is capable of generating functionally valid simulations for queuing and inventory management systems when provided with a verbal explanation. As a result, the language model could produce simulation models for inventory and process control. These results, along with the rapid improvement of language models, enable a significant simplification of simulation model development. Our study offers guidelines and a design of a natural language processing-based framework on how to build simulation models of logistics systems automatically, given the verbal description. In generalised terms, our work offers a technological underpinning of human-AI collaboration for the development of simulation models. [ABSTRACT FROM AUTHOR]

In this article, we provide a brief overview of generative artificial intelligence (GenAI) and large language models (LLMs). We then propose eight critical questions that libraries should ask when exploring this technology and its implications for their communities. We argue that libraries have a unique role in facilitating informed and responsible use of GenAI, as well as safeguarding and promoting the values of access, privacy, and intellectual freedom. [ABSTRACT FROM AUTHOR]

The Journal of Management Information Systems (JMIS) is celebrating its fifth decade of publication and is committed to publishing research papers that make significant contributions to the field of information systems. The journal's editors have an inclusive understanding of the field and aim to reflect and shape its growth and evolution. Over the years, JMIS has published papers on various topics, including the economics of IS, strategic deployment of IT, design research, neuroscience in IS research, behavioral cybersecurity, health informatics, and AI-human collaboration. The journal also recognizes its responsibility to support the well-being of society and explores the societal impacts of IT-driven change. The current issue features papers on topics such as the use of body-worn cameras in policing, technological innovation, crowdfunding, chatbots, sentiment analysis, cloud storage and security breaches, cybersecurity risk management, software architecture, and turnover of IT professionals. [Extracted from the article]

Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. Chatbots, serving as interactive conversational models, will probably drive this patient-centered transformation in healthcare. Through the provision of various services, including diagnosis, personalized lifestyle recommendations, dynamic scheduling of follow-ups, and mental health support, the objective is to substantially augment patient health outcomes, all the while mitigating the workload burden on healthcare providers. The life-critical nature of healthcare applications necessitates establishing a unified and comprehensive set of evaluation metrics for conversational models. Existing evaluation metrics proposed for various generic large language models (LLMs) demonstrate a lack of comprehension regarding medical and health concepts and their significance in promoting patients’ well-being. Moreover, these metrics neglect pivotal user-centered aspects, including trust-building, ethics, personalization, empathy, user comprehension, and emotional support. The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare. Subsequently, we present a comprehensive set of evaluation metrics designed to thoroughly assess the performance of healthcare chatbots from an end-user perspective. These metrics encompass an evaluation of language processing abilities, impact on real-world clinical tasks, and effectiveness in user-interactive conversations. Finally, we engage in a discussion concerning the challenges associated with defining and implementing these metrics, with particular emphasis on confounding factors such as the target audience, evaluation methods, and prompt techniques involved in the evaluation process.

"You Have to Live: Why Not Win?" is an autobiographical graphic novel by Dave Ketchen and Larry Thornton that tells the story of Larry Thornton, an older black entrepreneur and artist who owns several McDonald's restaurants in Birmingham, AL. The book explores Thornton's journey, overcoming obstacles, and learning valuable lessons in entrepreneurship and leadership. It also delves into the impact of growing up in the segregated southern United States and the importance of inclusivity and diverse perspectives in business education. The book's concise and visually powerful storytelling makes it a valuable resource for students and faculty in business schools. [Extracted from the article]

This article presents a discussion on generative artificial intelligence use in academia from the editors of the Academy of Management Journal. Topics include applications of generative artificial intelligence in academics--including knowledge synthesis and knowledge translation--as well as scenario planning of its use with varying levels of systems transparency and societal regulation.

We aim to investigate how closely neural networks (NNs) mimic human thinking. As a step in this direction, we study the behavior of artificial neuron(s) that fire most when the input data score high on some specific emergent concepts. In this paper, we focus on music, where the emergent concepts are those of rhythm, pitch and melody as commonly used by humans. As a black box to pry open, we focus on Google's MusicVAE, a pre-trained NN that handles music tracks by encoding them in terms of 512 latent variables. We show that several hundreds of these latent variables are "irrelevant" in the sense that can be set to zero with minimal impact on the reconstruction accuracy. The remaining few dozens of latent variables can be sorted by order of relevance by comparing their variance. We show that the first few most relevant variables, and only those, correlate highly with dozens of human-defined measures that describe rhythm and pitch in music pieces, thereby efficiently encapsulating many of these human-understandable concepts in a few nonlinear variables. [ABSTRACT FROM AUTHOR]

Cephalo is presented as a series of multimodal vision large language models (V‐LLMs) designed for materials science applications, integrating visual and linguistic data for enhanced understanding. A key innovation of Cephalo is its advanced dataset generation method. Cephalo is trained on integrated image and text data from thousands of scientific papers and science‐focused Wikipedia data demonstrates it can interpret complex visual scenes, generate precise language descriptions, and answer queries about images effectively. The combination of a vision encoder with an autoregressive transformer supports multimodal natural language understanding, which can be coupled with other generative methods to create an image‐to‐text‐to‐3D pipeline. To develop more capable models from smaller ones, both mixture‐of‐expert methods and model merging are reported. The models are examined in diverse use cases that incorporate biological materials, fracture and engineering analysis, protein biophysics, and bio‐inspired design based on insect behavior. Generative applications include bio‐inspired designs, including pollen‐inspired architected materials, as well as the synthesis of bio‐inspired material microstructures from a photograph of a solar eclipse. Additional model fine‐tuning with a series of molecular dynamics results demonstrate Cephalo's enhanced capabilities to accurately predict statistical features of stress and atomic energy distributions, as well as crack dynamics and damage in materials. [ABSTRACT FROM AUTHOR]

Objective: The integration of artificial intelligence (AI) in healthcare education is inevitable. Understanding the proficiency of generative AI in different languages to answer complex questions is crucial for educational purposes. The study objective was to compare the performance ChatGPT-4 and Gemini in answering Virology multiple-choice questions (MCQs) in English and Arabic, while assessing the quality of the generated content. Both AI models' responses to 40 Virology MCQs were assessed for correctness and quality based on the CLEAR tool designed for evaluation of AI-generated content. The MCQs were classified into lower and higher cognitive categories based on the revised Bloom's taxonomy. The study design considered the METRICS checklist for the design and reporting of generative AI-based studies in healthcare. Results: ChatGPT-4 and Gemini performed better in English compared to Arabic, with ChatGPT-4 consistently surpassing Gemini in correctness and CLEAR scores. ChatGPT-4 led Gemini with 80% vs. 62.5% correctness in English compared to 65% vs. 55% in Arabic. For both AI models, superior performance in lower cognitive domains was reported. Both ChatGPT-4 and Gemini exhibited potential in educational applications; nevertheless, their performance varied across languages highlighting the importance of continued development to ensure the effective AI integration in healthcare education globally. [ABSTRACT FROM AUTHOR]

This document provides concise summaries of several articles and research studies related to dermatology and contact allergies. The topics covered include non-compliance with nickel regulations, the association between atopic dermatitis and multimorbidity, contact sensitization to rubber additives, the long-term effectiveness of dupilumab treatment for atopic dermatitis, co-sensitizations and cross-reactivities to textile dyes, propolis sensitization and impurities, and various studies on contact allergies and patch testing. These studies offer valuable insights into the prevalence, causes, and treatment of dermatological conditions and contact allergies. [Extracted from the article]

This research involved designing and building an interactive generative AI application to conduct a comparative analysis of two advanced Large Language Models (LLMs), GPT‐4, and Claude 2, using Langsmith evaluation tools. The project was developed to explore the potential of LLMs in facilitating postgraduate course recommendations within a simulated environment at Munster Technological University (MTU). Designed for comparative analysis, the application enables testing of GPT‐4 and Claude 2 and can be hosted flexibly on either Amazon Web Services (AWS) or Azure. It utilizes advanced natural language processing and retrieval‐augmented generation (RAG) techniques to process proprietary data tailored to postgraduate needs. A key component of this research was the rigorous assessment of the LLMs using the Langsmith evaluation tool against both customized and standard benchmarks. The evaluation focused on metrics such as bias, safety, accuracy, cost, robustness, and latency. Additionally, adaptability covering critical features like language translation and internet access, was independently researched since the Langsmith tool does not evaluate this metric. This ensures a holistic assessment of the LLM's capabilities. [ABSTRACT FROM AUTHOR]

In the evolving landscape of teacher education, where generative AI poses both opportunities and challenges, this article investigates the resurgence of orality through podcasting as a pedagogical tool. Situated within a teacher education program, the study focuses on teacher candidates who are navigating the complexities of educational technologies. It examines the role of student created podcasts for enhancing learning experiences, fostering collaborative communities, and developing essential teaching skills. Drawing on Walter Ong's theory of orality and literacy, this phenomenological research explores how a digital return to orality can effectively counterbalance the impersonal nature of AI-generated content in education. The study argues that podcasting embodies human craftsmanship, revitalizes oral traditions in learning, and equips future educators with innovative pedagogical strategies in an increasingly digital academic environment. [ABSTRACT FROM AUTHOR]

Neurodegenerative dementia syndromes, such as primary progressive aphasias (PPA), have traditionally been diagnosed based, in part, on verbal and non-verbal cognitive profiles. Debate continues about whether PPA is best divided into three variants and regarding the most distinctive linguistic features for classifying PPA variants. In this cross-sectional study, we initially harnessed the capabilities of artificial intelligence and natural language processing to perform unsupervised classification of short, connected speech samples from 78 pateints with PPA. We then used natural language processing to identify linguistic features that best dissociate the three PPA variants. Large language models discerned three distinct PPA clusters, with 88.5% agreement with independent clinical diagnoses. Patterns of cortical atrophy of three data-driven clusters corresponded to the localization in the clinical diagnostic criteria. In the subsequent supervised classification, 17 distinctive features emerged, including the observation that separating verbs into high- and low-frequency types significantly improved classification accuracy. Using these linguistic features derived from the analysis of short, connected speech samples, we developed a classifier that achieved 97.9% accuracy in classifying the four groups (three PPA variants and healthy controls). The data-driven section of this study showcases the ability of large language models to find natural partitioning in the speech of patients with PPA consistent with conventional variants. In addition, the work identifies a robust set of language features indicative of each PPA variant, emphasizing the significance of dividing verbs into high- and low-frequency categories. Beyond improving diagnostic accuracy, these findings enhance our understanding of the neurobiology of language processing. [ABSTRACT FROM AUTHOR]

Objectives The efficacy of artificial intelligence (AI)-driven chatbots like ChatGPT4 in specialized medical consultations, particularly in rheumatology, remains underexplored. This study compares the proficiency of ChatGPT4' responses with practicing rheumatologists to inquiries from patients with SLE. Methods In this cross-sectional study, we curated 95 frequently asked questions (FAQs), including 55 in Chinese and 40 in English. Responses for FAQs from ChatGPT4 and five rheumatologists were scored separately by a panel of rheumatologists and a group of patients with SLE across six domains (scientific validity, logical consistency, comprehensibility, completeness, satisfaction level and empathy) on a 0–10 scale (a score of 0 indicates entirely incorrect responses, while 10 indicates accurate and comprehensive answers). Results Rheumatologists' scoring revealed that ChatGPT4-generated responses outperformed those from rheumatologists in satisfaction level and empathy, with mean differences of 0.537 (95% CI, 0.252–0.823; P   <  0.01) and 0.460 (95% CI, 0.227–0.693; P   <  0.01), respectively. From the SLE patients' perspective, ChatGPT4-generated responses were comparable to the rheumatologist-provided answers in all six domains. Subgroup analysis revealed ChatGPT4 responses were more logically consistent and complete regardless of language and exhibited greater comprehensibility, satisfaction and empathy in Chinese. However, ChatGPT4 responses were inferior in comprehensibility for English FAQs. Conclusion ChatGPT4 demonstrated comparable, possibly better in certain domains, to address FAQs from patients with SLE, when compared with the answers provided by specialists. This study showed the potential of applying ChatGPT4 to improve consultation in SLE patients. [ABSTRACT FROM AUTHOR]

Generative artificial intelligence (AI) has rapidly increased capacity for producing textual, visual and auditory outputs, yet there are ongoing concerns regarding the quality of those outputs. There is an urgent need to develop students' evaluative judgement – the capability to judge the quality of work of self and others – in recognition of this new reality. In this conceptual paper, we describe the intersection between evaluative judgement and generative AI with a view to articulating how assessment practices can help students learn to work productively with generative AI. We propose three foci: (1) developing evaluative judgement of generative AI outputs; (2) developing evaluative judgement of generative AI processes; and (3) generative AI assessment of student evaluative judgements. We argue for developing students' capabilities to identify and calibrate quality of work – uniquely human capabilities at a time of technological acceleration – through existing formative assessment strategies. These approaches circumvent and interrupt students' uncritical usage of generative AI. The relationship between evaluative judgement and generative AI is more than just the application of human judgement to machine outputs. We have a collective responsibility, as educators and learners, to ensure that humans do not relinquish their roles as arbiters of quality. [ABSTRACT FROM AUTHOR]

Automation is a defining feature of today's societies—not only since ChatGPT and generative artificial intelligence (AI) have accomplished to produce yet another wave of hype. This essay introduces a special issue on automation and communication in the digital society. It aims to study how subjectivity, agency, and empowerment become defined and reconfigured in novel human–machine encounters and, more broadly, in societies which in large parts are kept going and sustained by complex digital infrastructures. The issue includes contributions from a wide array of disciplines and perspectives and engages with conditions, contexts, and consequences of automation in very different settings ranging from journalism to self-service hotels, and from social movements in Hong Kong to the Russian Invasion to the Ukraine. The articles offer critical perspectives on the transition of human activity into machine operations, and back, as well on the social dynamics changing and emerging in increasingly digitized and datafied societies. [ABSTRACT FROM AUTHOR]

This article showcases a speculative methodology for recreating interactions between a human and Google Search's Auto-Predict interface as conversations, to explore how AI-based systems are both persuasive and deeply personal. Using ethnomethodology tools and a symbolic interactionist lens, the paper presents three versions of a single Google search, each variation building a slightly different angle on the plausible utterances and interpersonal dynamics of the human and nonhuman partners. This thought experiment emerges from a decade of classroom-based digital literacy exercises with young adults, training them to analyze their lived experiences with digital media, algorithms, and devices. Transforming information exchanges into personal conversations provides a creative method for analyzing how relations are co-constructed in the granular processes of interaction, through which mutual intelligibility is built, meaning about the world is made, and identities are formed. This critical analysis extends methods for human–machine communication studies and elaborates notions of algorithmic identity. [ABSTRACT FROM AUTHOR]

A key goal of educational institutions around the world is to provide inclusive, equitable quality education and lifelong learning opportunities for all learners. Achieving this requires contextualized approaches to accommodate diverse global values and promote learning opportunities that best meet the needs and goals of all learners as individuals and members of different communities. Advances in learning analytics (LA), natural language processes (NLP), and artificial intelligence (AI), especially generative AI technologies, offer potential to aid educational decision making by supporting analytic insights and personalized recommendations. However, these technologies also raise serious risks for reinforcing or exacerbating existing inequalities; these dangers arise from multiple factors including biases represented in training datasets, the technologies' abilities to take autonomous decisions, and processes for tool development that do not centre the needs and concerns of historically marginalized groups. To ensure that Educational Decision Support Systems (EDSS), particularly AI‐powered ones, are equipped to promote equity, they must be created and evaluated holistically, considering their potential for both targeted and systemic impacts on all learners, especially members of historically marginalized groups. Adopting a socio‐technical and cultural perspective is crucial for designing, deploying, and evaluating AI‐EDSS that truly advance educational equity and inclusion. This editorial introduces the contributions of five papers for the special section on advancing equity and inclusion in educational practices with AI‐EDSS. These papers focus on (i) a review of biases in large language models (LLMs) applications offers practical guidelines for their evaluation to promote educational equity, (ii) techniques to mitigate disparities across countries and languages in LLMs representation of educationally relevant knowledge, (iii) implementing equitable and intersectionality‐aware machine learning applications in education, (iv) introducing a LA dashboard that aims to promote institutional equality, diversity, and inclusion, and (v) vulnerable student digital well‐being in AI‐EDSS. Together, these contributions underscore the importance of an interdisciplinary approach in developing and utilizing AI‐EDSS to not only foster a more inclusive and equitable educational landscape worldwide but also reveal a critical need for a broader contextualization of equity that incorporates the socio‐technical questions of what kinds of decisions AI is being used to support, for what purposes, and whose goals are prioritized in this process. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) are increasingly adopted in educational contexts to provide personalized support to students and teachers. The unprecedented capacity of LLM‐based applications to understand and generate natural language can potentially improve instructional effectiveness and learning outcomes, but the integration of LLMs in education technology has renewed concerns over algorithmic bias, which may exacerbate educational inequalities. Building on prior work that mapped the traditional machine learning life cycle, we provide a framework of the LLM life cycle from the initial development of LLMs to customizing pre‐trained models for various applications in educational settings. We explain each step in the LLM life cycle and identify potential sources of bias that may arise in the context of education. We discuss why current measures of bias from traditional machine learning fail to transfer to LLM‐generated text (eg, tutoring conversations) because text encodings are high‐dimensional, there can be multiple correct responses, and tailoring responses may be pedagogically desirable rather than unfair. The proposed framework clarifies the complex nature of bias in LLM applications and provides practical guidance for their evaluation to promote educational equity. Practitioner notesWhat is already known about this topic The life cycle of traditional machine learning (ML) applications which focus on predicting labels is well understood.Biases are known to enter in traditional ML applications at various points in the life cycle, and methods to measure and mitigate these biases have been developed and tested.Large language models (LLMs) and other forms of generative artificial intelligence (GenAI) are increasingly adopted in education technologies (EdTech), but current evaluation approaches are not specific to the domain of education.What this paper adds A holistic perspective of the LLM life cycle with domain‐specific examples in education to highlight opportunities and challenges for incorporating natural language understanding (NLU) and natural language generation (NLG) into EdTech.Potential sources of bias are identified in each step of the LLM life cycle and discussed in the context of education.A framework for understanding where to expect potential harms of LLMs for students, teachers, and other users of GenAI technology in education, which can guide approaches to bias measurement and mitigation.Implications for practice and/or policy Education practitioners and policymakers should be aware that biases can originate from a multitude of steps in the LLM life cycle, and the life cycle perspective offers them a heuristic for asking technology developers to explain each step to assess the risk of bias.Measuring the biases of systems that use LLMs in education is more complex than with traditional ML, in large part because the evaluation of natural language generation is highly context‐dependent (eg, what counts as good feedback on an assignment varies).EdTech developers can play an important role in collecting and curating datasets for the evaluation and benchmarking of LLM applications moving forward. [ABSTRACT FROM AUTHOR]