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Guided by IntelliBloom: Promoting artificial intelligence literacy among teachers in higher education
Abstract
As artificial intelligence (AI) continues to permeate higher education, it can be valuable for teachers to develop AI literacy to bring AI technologies into their teaching practices. However, the rapid advancement of AI has left many teachers overwhelmed and uncertain about its potential for education. They often experience a lack of starting points and guidance to develop a meaningful understanding of AI’s potential applications and benefits for teaching and learning. To bridge this gap, this study introduces IntelliBloom, a theoretically and empirically validated guided process designed to assist teachers in integrating educational AI services (EAISs) into their pedagogical practices. This article serves to promote AI literacy among teachers in higher education and forms a basis for further discussion on how AI can be used to enhance teaching and learning practices.
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Artificial intelligence (AI) has gained attention as one of the emerging fields in educational technology. The growing body of AI research has focused primarily on technological perspectives and less focus has been given to facilitate the development of AI-driven practices in higher education. In this article, we explore and provide insights together with practical examples into how AI through flexible adaptation could be integrated as practices in higher education using Bloom's digital taxonomy as an educational model. The resulting operationalization is presented in the form of six practices that provide a basis for discussing how AI-powered innovations can be used in higher education.
Since 2020, teaching has gone through substantive transformation. Teacher educators have been compelled to reevaluate their instructional strategies and the use of technology to deliver effective training for pre-service teachers. The year 2020 escalated the use of teaching strategies that relied on technology as a crucial tool in all teaching processes. Moreover, from the year 2020 and onwards, teacher educators’ knowledge of teaching, curriculum, and technology has been transformed to align with the new teaching and learning requirements for creating and delivering instruction in teacher education. However, little is mentioned in studies on how teacher educators think about lesson activities, especially how they map out instructional pathways and technology integration in a technology-rich environment. It is against this backdrop that this study uses qualitative and interpretive paradigms to investigate teacher educators’ use of mind-mapping for instructional practices in a technology-rich environment. The objective was to understand how they develop or prioritize the development of technological pedagogical content knowledge (TPACK). Six participants were purposively selected based on the criteria that the participants must use mind-mapping to plan lesson activities and integrate technology into the lesson, reflect on the instructional process, and allow the researchers to observe their classroom teaching engagements. Data was generated and collected using journal reflections, observations, and document analysis. The findings showed that the teacher educators made use of mind-mapping in the preparation of lesson activities and created rubrics using mind-mapping. They also used self-made video podcasts and integrated videos in lesson activities and mind-mapped assessment pathways with technology. From the narrations shared by the participants, the use of mind-mapping opened new strategies of planning, teaching, and assessing instruction with technology to develop technological pedagogical content knowledge.
The interest in artificial intelligence (AI) in education has erupted during the last few years, primarily due to technological advances in AI. It is therefore argued that students should learn about AI, although it is debated exactly how it should be applied in education. AI literacy has been suggested as a way of defining competencies for students to acquire to meet a future everyday- and working life with AI. This study argues that researchers and educators need a framework for integrating AI literacy into technological literacy, where the latter is viewed as a multiliteracy. This study thus aims to critically analyse and discuss different components of AI literacy found in the literature in relation to technological literacy. The data consists of five AI literacy frameworks related to three traditions of technological knowledge: technical skills, technological scientific knowledge, and socio-ethical technical understanding. The results show that AI literacy for technology education emphasises technological scientific knowledge (e.g., knowledge about what AI is, how to recognise AI, and systems thinking) and socio-ethical technical understanding (e.g., AI ethics and the role of humans in AI). Technical skills such as programming competencies also appear but are less emphasised. Implications for technology education are also discussed, and a framework for AI literacy for technology education is suggested.
Moving beyond the direct support all alone by a human teacher or an Artificial Intelligence (AI) system, optimizing the complementary strengths of the two has aroused great expectations and educational innovation potential. Yet, the conceptual guidance of how best to structure and implement teacher-AI collaboration (TAC) while ensuring teachers' instructional roles to support students learning remains limited. This study, therefore, aims what (1) curriculum, (2) teacher-AI interaction, (3) learning environment would be required as well as how TAC would evolve by reflecting teachers' views. Through in-depth interviews with 20 Chinese leading teachers in AI in Education (AIED), the study found that teachers aimed to improve students' subject-matter knowledge and build their capacity as the desired goals for TAC and these can be carried out by data-driven problem-based learning and case-based reasoning in tandem with growth-focused and reflective assessment. While teachers highlighted that developing teachers' data literacy and collegiality with AI are essential, they expected AI to be equipped with Technological Pedagogical and Content Knowledge (TPACK) knowledge and conflict resolution skills. In addition, teachers expressed that Internet of Things (IoT)-based classrooms, systematic AIED curriculum, school-based continuing professional development, research-practice-policy partnerships as well as creating a continuous learning and AI-ready culture are significant. Furthermore, teachers envision TAC would develop into three stages: (1) teachers as passive AI recipients, (2) teachers as active AI users (3) teachers-AI as constructive partners. These findings build a more holistic and in-depth understanding of the AIED and offer implications for the educational AI design and teachers' education.
A better understanding of educators’ perspectives of emerging education technology, specifically tools that incorporate AI, is essential to unlock the full potential benefits of these innovations. While prior research has primarily emphasized technological advancements, it has overlooked the profound influence of social, psychological, and cultural factors in shaping educators’ perceptions, trust, and adoption of educational technology. As increasingly powerful AI tools emerge, their design must be rooted in a deep understanding of educators' needs and perspectives. It is only with the acceptance and trust of educators that these innovative solutions can elevate learning outcomes, academic achievements, and educational equity.
Large Language Models (LLMs) and conversational-style generative artificial intelligence (AI) are causing major disruption to higher education pedagogy. The emergence of tools like ChatGPT has raised concerns about plagiarism detection but also presents opportunities for educators to leverage AI to build supportive learning environments. In this commentary, we explore the potential of AI-augmented teaching and learning practice in higher education, discussing both the productive affordances and challenges associated with these technologies. We offer instructional advice for writing instructional text to guide the generation of quality outputs from AI models, as well as a case study to illustrate using AI for assessment design. Ultimately, we suggest that AI should be seen as one tool among many that can be used to enhance teaching and learning outcomes in higher education.
In a world driven by constant change and innovation, Higher Education Institutions (HEIs) are undergoing a rapid transformation, often driven by external factors such as emerging technologies. One of the key drivers affecting the design and development of educational delivery mechanisms in HEIs is the fast pace of educational technology development which not only impacts an institution’s technical capacity to infuse hardware and software solutions into existing learning infrastructure but also has implications for pedagogical practice, stakeholder acceptance of new technology, and HEI administrative structures. However, little is known about the implementation of contemporary educational technology in HEI environments, particularly as they relate to competing stakeholder perceptions of technology effectiveness in course delivery and knowledge acquisition. This review fills that gap by exploring the evidence and analyses of 46 empirical research studies focussing on technology implementation issues in a diverse range of institutional contexts, subject areas, technologies, and stakeholder profiles. This study found that the dynamic interplay of educational technology characteristics, stakeholder perceptions on the effectiveness of technology integration decisions, theoretical frameworks and models relevant to technology integration in pedagogical practices, and metrics to gauge post-implementation success are critical dimensions to creating viable pathways to effective educational technology implementation. To that end, this study proposes a framework to guide the development of sound implementation strategies that incorporates five dimensions: technology, stakeholder perceptions, academic discipline, success metrics, and theoretical frameworks. This study will benefit HEI decision-makers responsible for re-engineering complex course delivery systems to accommodate the infusion of new technologies and pedagogies in ways that will maximise their utility to students and faculty.
The application of Artifcial Intelligence (AI) across a wide range of domains comes with both high expectations of its benefts and dire predictions of misuse. While AI systems have largely been driven by a technology-centered design approach, the potential societal conse- quences of AI have mobilized both HCI and AI researchers towards researching human-centered artifcial intelligence (HCAI). How- ever, there remains considerable ambiguity about what it means to frame, design and evaluate HCAI. This paper presents a critical review of the large corpus of peer-reviewed literature emerging on HCAI in order to characterize what the community is defning as HCAI. Our review contributes an overview and map of HCAI research based on work that explicitly mentions the terms ‘human- centered artifcial intelligence’ or ‘human-centered machine learn- ing’ or their variations, and suggests future challenges and research directions. The map reveals the breadth of research happening in HCAI, established clusters and the emerging areas of Interaction with AI and Ethical AI. The paper contributes a new defnition of HCAI, and calls for greater collaboration between AI and HCI research, and new HCAI constructs.
This research article crafted, evaluated, and revised a theoretically underpinned design concept with the purpose of enhancing customers' dine-in experiences. The design concept was motivated by the considerable interest in artificial intelligence (AI), voice user interfaces (VUI) within Human-Computer Interaction (HCI), and the rapid digitalization of online food ordering as a result of the COVID-19 pandemic. The study applied the concept-driven design research approach because it offered to make theoretical contributions while at the same time being design and concept-oriented. The result of this research is a revised design concept that has the potential to digitalize the dine-in restaurant business further and add to the understanding of human experience while interacting with a voice user interface. Finally, the research article manifests as an example of how interaction designers make theoretical contributions through design and how technologies can be combined in new contexts.
Adopting Artificial Intelligent Technology in an Educational Organization is often problematic due to many internal and external environmental reasons, and often fails to attain the desired goals. This study aims to design a framework for adopting AI technology in the education sector. Most of the research focuses on the acceptance of a particular technology and ignores the study of what else is needed for a technology acceptance. The framework in this study provides a step-by-step process of the Technological Transformation of an organization never designed before. We recommend that before making any technological changes in an organization, generally and in the educational organization particularly, the processes must be followed for the successful and meaningful adoption of AI technology.
Applications of artificial intelligence in education (AIEd) are emerging and are new to researchers and practitioners alike. Reviews of the relevant literature have not examined how AI technologies have been integrated into each of the four key educational domains of learning, teaching, assessment, and administration. The relationships between the technologies and learning outcomes for students and teachers have also been neglected. This systematic review study aims to understand the opportunities and challenges of AIEd by examining the literature from the last 10 years (2012–2021) using matrix coding and content analysis approaches. The results present the current focus of AIEd research by identifying 13 roles of AI technologies in the key educational domains, 7 learning outcomes of AIEd, and 10 major challenges. The review also provides suggestions for future directions of AIEd research.
As smart technology promotes the development of various industries, artificial intelligence (AI) has also become an important driving force for innovation and transformation in education. For teachers, how to skillfully apply AI in teaching and improve their AI literacy has become a necessary goal for their sustainable professional development. This research examines the correlations among the dimensions of AI literacy of teachers in order to promote the effectiveness of class teaching and the adoption of artificial intelligence literacy (AIL). Our findings are based on the analysis of 1013 survey results, where we tested the level of AI literacy of teachers, including Knowing and Understanding AI (KUAI), Applying AI (AAI), Evaluating AI Application (EAIA), and AI Ethics (AIE). We find that AAI had a significant, positive effect on the other three dimensions. Thus, based on the analysis, the government should take action to cultivate teachers’ AI literacy. In order to improve teachers’ AI literacy, the choice of curriculum, content, methods, and practical resources for special training should be diverse and committed to making AI literacy an essential enabler for teachers’ sustainable future development.
Since artificial intelligence (AI) is finding its way into more and more areas of everyday life, improving the AI skills of non-experts is important and will become even more relevant in the future. While it is necessary that children learn about the possibilities of AI at an early age, adults in higher education and beyond should also have at least a basic understanding of AI (i.e., AI literacy) to be able to interact effectively with the technology. To evaluate the current state of the literature on AI literacy in higher and adult education, a scoping literature review was conducted with the objective of identifying thematic foci and recent research trends. Ten research databases were searched and out of 902 initial records, 30 studies were identified using predefined eligibility criteria, whereof the content was evaluated in the review. The results indicated that research in this area is still in its infancy and needs refinement in terms of how to define AI literacy in adult education as well as what content should be taught to non-experts. Lastly, several recommendations for research and practice are derived from the results.
The affordances of artificial intelligence (AI) have not been totally utilized in education. To effectively integrate AI into education, teachers’ AI-specific technological and pedagogical knowledge is important. Furthermore, due to novel ethical issues caused by Al, teachers also must have the knowledge to assess AI-based decisions. None of the previous studies so far explored teacher knowledge to pedagogically and ethically use AI-based tools. Considering this gap, we first developed a scale to measure the knowledge for instructional AI use based on the technological, pedagogical, and content knowledge (TPACK) framework. We extended TPACK with ethical aspects. Secondly, we built a model to investigate the interplay of TPACK components and ethics. The results indicated that as long as teachers have more knowledge to interact with AI-based tools, they will have a better understanding of the pedagogical contributions of AI. Further, technological knowledge (TK) allows teachers to better assess decisions of AI. However, only TK is not sufficient educational integration of AI-based tools. For teachers to deploy AI in education efficiently, TK is meaningful when it is combined with pedagogical knowledge (PK), reflected in technological pedagogical knowledge (TPK). Given pedagogical and technological affordances of AI-based tools, the current study suggests the Intelligent-TPACK framework.
In this paper, we present the concept of AI Readiness, along with a framework for developing AI Readiness training. ‘AI Readiness’ can be framed as a contextualised way of helping people to understand AI, in particular, data-driven AI. The nature of AI Readiness training is not the same as merely learning about AI. Rather, AI Readiness recognises the diversity of the professions, workplaces and sectors for whom AI has a potential impact. For example, AI Readiness for lawyers may be based on the same principles as AI Readiness for Educators. However, the details will be contextualised differently. AI Readiness recognises that such contextualisation is not an option: it is essential due to the multiple intricacies, sensitivities and variations between different sectors and their settings, which all impact the application of AI. To embrace such contextualisation, AI Readiness needs to be an active, participatory training process and aims to empower people to be more able to leverage AI to meet their needs.
The text that follows focuses on AI Readiness within the Education and Training sector and starts with a discussion of the current state of AI within education and training, and the need for AI Readiness. We then problematize the concept of AI Readiness, why AI Readiness is needed, and what it means. We expand upon the nature of AI Readiness through a discussion of the difference between human and Artificial Intelligence, before presenting a 7-step framework for helping people to become AI Ready. Finally, we use an example of AI Readiness in action within Higher Education to exemplify AI Readiness.
Distance education has been the concern of educational institutions nowadays due to COVID 19 pandemic. The purpose of this study which was carried out during COVID-19 pandemic lockdown period in 2020 was to evaluate the effectiveness of webcast applications on teacher training. Since it was a sudden and an unpredicted transition from face-to-face education to webcast education, it was crucial to evaluate the outcome of teaching applications in order to compensate for the missing points in education. The evaluation framework in this study was based on Bloom’s Digital Taxonomy. The participants’ responses demonstrated that as a consequence of the webcast application in education, remembering, understanding, and analyzing skills were activated better than applying, evaluating, and creating thinking skills in the digital taxonomy. The findings would not be applicable to all webcast training but only to the course design that was delivered as an emergency remote course in a global pandemic.
AI-powered educational technology that is designed to support teachers in providing personalized instruction can enhance their ability to address the needs of individual students, hopefully leading to better learning gains. This paper presents results from a participatory research aimed at co-designing with science teachers a learning analytics tool that will assist them in implementing a personalized pedagogy in blended learning contexts. The development process included three stages. In the first, we interviewed a group of teachers to identify where and how personalized instruction may be integrated into their teaching practices. This yielded a clustering-based personalization strategy. Next, we designed a mock-up of a learning analytics tool that supports this strategy and worked with another group of teachers to define an ‘explainable learning analytics’ scheme that explains each cluster in a way that is both pedagogically meaningful and can be generated automatically. Third, we developed an AI algorithm that supports this ‘explainable clusters’ pedagogy and conducted a controlled experiment that evaluated its contribution to teachers’ ability to plan personalized learning sequences. The planned sequences were evaluated in a blinded fashion by an expert, and the results demonstrated that the experimental group – teachers who received the clusters with the explanations – designed sequences that addressed the difficulties exhibited by different groups of students better than those designed by teachers who received the clusters without explanations. The main contribution of this study is twofold. First, it presents an effective personalization approach that fits blended learning in the science classroom, which combines a real-time clustering algorithm with an explainable-AI scheme that can automatically build pedagogically meaningful explanations from item-level meta-data (Q Matrix). Second, it demonstrates how such an end-to-end learning analytics solution can be built with teachers through a co-design process and highlights the types of knowledge that teachers add to system-provided analytics in order to apply them to their local context. As a practical contribution, this process informed the design of a new learning analytics tool that was integrated into a free online learning platform that is being used by more than 1000 science teachers.
The digitalization of education at the turn of the century has necessitated a digital update of Benjamin Bloom's original taxonomy of educational objectives (1956) and of Anderson and Krathwohl's revised taxonomy (2001). The updated digital taxonomy (Churches, 2008) captures the continually emerging learning opportunities in technology-enhanced language classrooms. The present paper maps Bloom's updated digital taxonomy onto a set of learning pedagogies characterized by negotiation and/or production in the implementation of a Web 2.0-enabled and project-based learning design in online college education. The study reports on ten projects designed by the researcher to facilitate acquisition of specialized content and skills prescribed in a college-level English program in Saudi Arabia. Based on the nature of the content and skills to learn, an appropriate learning pedagogy and a set of suitable Web 2.0 tools were mapped in the design of project-based learning tasks differentiated to the students' level, 11 interests and familiarity with digital tools. The projects were produced individually and collaboratively by the students outside class and presented individually and collaboratively on a virtual learning platform. The learning design employed in the study mobilized the students' digital competence and enabled them to demonstrate acquisition of knowledge and skills in different modalities (text, image, audio and video). It also engaged the students in content creation, facilitated synchronous and remote collaboration in the preparation and presentation of projects, created authentic out-of-class learning opportunities, encouraged active learning, and enabled learning at the highest level of Bloom's taxonomy.
We discuss the new challenges and directions facing the use of big data and artificial intelligence (AI) in education research, policy-making, and industry. In recent years, applications of big data and AI in education have made significant headways. This highlights a novel trend in leading-edge educational research. The convenience and embeddedness of data collection within educational technologies, paired with computational techniques have made the analyses of big data a reality. We are moving beyond proof-of-concept demonstrations and applications of techniques, and are beginning to see substantial adoption in many areas of education. The key research trends in the domains of big data and AI are associated with assessment, individualized learning, and precision education. Model-driven data analytics approaches will grow quickly to guide the development, interpretation, and validation of the algorithms. However, conclusions from educational analytics should, of course, be applied with caution. At the education policy level, the government should be devoted to supporting lifelong learning, offering teacher education programs, and protecting personal data. With regard to the education industry, reciprocal and mutually beneficial relationships should be developed in order to enhance academia-industry collaboration. Furthermore, it is important to make sure that technologies are guided by relevant theoretical frameworks and are empirically tested. Lastly, in this paper we advocate an in-depth dialog between supporters of “cold” technology and “warm” humanity so that it can lead to greater understanding among teachers and students about how technology, and specifically, the big data explosion and AI revolution can bring new opportunities (and challenges) that can be best leveraged for pedagogical practices and learning.
Purpose
This paper intended to explore the knowledge and use of the digital verbs and tools by the students and teachers for conceptual understanding in the virtual and conventional learning environment. The study also explored the use of such digital tools for lower- and higher-order thinking.
Design/methodology/approach
A survey research method was used for the study. All the students and teachers of the faculty of education from one virtual and one conventional university were the population of this study. Teachers were selected through census sampling. Student enrollment in the faculty of education of the virtual university during Spring 2019 was 1,139 while the conventional university had 1,809 students. In total, 20% of the students from each of the two universities were sampled by using a convenient proportionate sampling technique. A questionnaire was developed by the researchers and validated by three experts before administration. The reliability of the instrument was a = 0.934. Mean, SD, parametric and nonparametric statistics were applied for data analysis.
Findings
The study reveals that the students of ODL are far better in using digital tools and activities that is, googling, collaborating and Skyping. They are good at understanding and application levels and are involved in higher-order thinking tasks, that is, publishing and podcasting as well. Unlike the students, the teachers of the virtual university are using digital tools of lower-order thinking. The authors infer that the students and teachers of the online universities are using these tools regularly because of the demands of the ODL environment. These findings suggest further research to explore the factors that hinder the use of higher-order thinking skills by the teachers in the online environment.
Originality/value
The study suggests the adoption of Bloom's digital taxonomy in teaching–learning processes, that is, curriculum, instructions and assessment for the millennials. The findings may motivate the online and conventional higher education institutions to adopt digital pedagogy for instructional purposes as the students of the digital age are already extensively involved with digital tools.
The Covid-19 pandemic has raised significant challenges for the higher education community worldwide. A particular challenge has been the urgent and unexpected request for previously face-to-face university courses to be taught online. Online teaching and learning imply a certain pedagogical content knowledge (PCK), mainly related to designing and organising for better learning experiences and creating distinctive learning environments, with the help of digital technologies. With this article, we provide some expert insights into this online-learning-related PCK, with the goal of helping non-expert university teachers (i.e. those who have little experience with online learning) to navigate in these challenging times. Our findings point at the design of learning activities with certain characteristics, the combination of three types of presence (social, cognitive and facilitatory) and the need for adapting assessment to the new learning requirements. We end with a reflection on how responding to a crisis (as best we can) may precipitate enhanced teaching and learning practices in the postdigital era.
The dominance of technology over various aspects of life has changed the digital natives' style of learning too. There, however, exists no comprehensive educational-based instruction on how to work with this generation of learners in Virtual Learning Environments (VLEs). The purpose of this study – therefore - is to develop and validate educational standards and indicators of e-learning environments. The mixed-method design applied in this study comprises of a qualitative stage involving a phenomenological study and a quantitative stage employing a survey research method. Semi-structured interviews with 12 experienced e-learning instructors were conducted to collect the qualitative data and a researcher-made questionnaire was distributed amongst 171 e-learning experts in the quantitative stage. Qualitative data was analyzed using Corbin and Strauss’s coding and Maxqda 10 Software, and quantitative data was analysed using inferential statistics. As the outcome of this study, two educational standards and 18 indispensable indicators based on Bloom-Anderson Taxonomy were developed and validated. These findings contribute to e-learning literature by highlighting the pedagogical standards need to be met in order to get the best out of learning technologies. These standards and indicators can be either used as a guide to design or evaluate e-learning initiatives from a pedagogical perspective.
According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education.
Este artigo apresenta e discute brevemente alguns resultados de uma pesquisa realizada como parte de um
estudo sobre múltiplas literacias e o uso da tecnologia na aula de inglês como língua estrangeira (English
as a Foreign Language – EFL) (Cardoso, 2017). Um questionário online foi enviado a professores de inglês
do terceiro ciclo e secundário, em Portugal, com o objetivo de investigar as perceções e as opiniões deles a respeito do uso das novas tecnologias, especialmente dos recursos da Web 2.0, em sua prática profissional. O presente artigo enfoca particularmente os resultados obtidos das perguntas relacionadas à
frequência de uso de materiais e recursos, à visão dos professores sobre o uso da tecnologia, assim como
à abordagem das literacias digitais e críticas. Embora a tecnologia pareça fazer parte da prática discente desse grupo de professores, e ainda que pesquisas adicionais sejam necessárias para entender melhor o
uso real dessa tecnologia nesse cenário em particular, é possível dizer que são necessárias algumas medidas para que a integração significativa e eficaz das novas tecnologias nas salas de aula de EFL, tais como,
orientações adequadas e treinamento aos professores, e maior desenvolvimento de materiais apropriados.
Bloom's taxonomy is a model that allows characterizing students' learning achievements. It is frequently used in computer science education (CSE), but its use is not straightforward. We present a systematic review conducted to know actual use of the taxonomy in CSE. We found that it was mostly used in programming education and to assess students' performance. A more relevant contribution is a classification of authors' difficulties. In particular, the most often reported difficulty is determining the level of the taxonomy where an assessment task can be classified. In addition, we present authors' hypotheses about possible causes of the difficulties and the solutions they adopted.
Learning outcomes are essential to any curriculum in education, where they need to be clear, observable and measurable. However, some academics structure learning outcomes in a way that does not promote student learning. The purpose of this article is to present the analyses of learning outcomes of an Electrical Engineering curriculum offered at a University of Technology in South Africa, in order to determine if academics are structuring them in a way that enables student learning. A qualitative case study is used where the learning outcomes from 33 study guides are reviewed using illustrative verbs derived from Bloom’s Taxonomy. Results indicate that 9% of all the learning outcomes are unclear, 10% are unobservable and 23% are unmeasurable. A key recommendation is to provide regular workshops to assist academics in reviewing their learning outcomes using the illustrative verbs derived from Bloom’s Taxonomy, thereby ensuring that their learning outcomes promote student learning.
Though pedagogical beliefs have been identified as critical factors in the success of technology integration, very few studies have included them in technology-adoption models. The present study revises the Technology Acceptance Model (TAM) by adding teachers’ pedagogical beliefs, and tests the revised model among university-level English as a foreign language (EFL) teachers in China. Specifically, the revised model examines how teachers’ constructivist and/or transmissive pedagogical beliefs influence four key constructs of the TAM: perceived usefulness, perceived ease of use, attitude toward use, and intention to use. Survey data were collected from 202 Chinese EFL teachers and analyzed using path analysis. The revised model showed a good model fit. The results indicated that the sampled teachers’ pedagogical beliefs were more constructivist-oriented than transmissive-oriented, and that the former type of beliefs had a significant positive influence on three of the above-mentioned TAM constructs (perceived usefulness, perceived ease of use, and attitude toward use). Teachers’ transmissive pedagogical beliefs, on the other hand, did not have any significant impact on their attitudes toward information and communication technology (ICT) or their perceptions of its usefulness, though such beliefs did significantly affect their perceptions of how easy ICT was to use. Implications of these findings for teacher education and professional training are discussed.
In this article, we explore a concept-driven approach to interaction design research with a specific focus on theoretical advancements. We introduce this approach as a complementary approach to more traditional, and well-known, user-centered interaction design approaches. A concept-driven approach aims at manifesting theoretical concepts in concrete designs. A good concept design is both conceptually and historically grounded, bearing signs of the intended theoretical considerations. In the area of human–computer interaction and interaction design research, this approach has been quite popular but not necessarily explicitly recognized and developed as a proper research methodology. In this article, we demonstrate how a concept-driven approach can coexist, and be integrated with, common user-centered approaches to interaction design through the development of a model that makes explicit the existing cycle of prototyping, theory development, and user studies. We also present a set of basic principles that could constitute a foundation for concept driven interaction research, and we have considered and described the methodological implications given these principles. For the field of interaction design research we find this as an important point of departure for taking the next step toward the construction and verification of theoretical constructs that can help inform and guide future design research projects on novel interaction technologies.
Early studies indicated that teachers’ enacted beliefs, particularly in terms of classroom technology practices, often did not align with their espoused beliefs. Researchers concluded this was due, at least in part, to a variety of external barriers that prevented teachers from using technology in ways that aligned more closely with their beliefs. However, many of these barriers (access, support, etc.) have since been eliminated in the majority of schools. This multiple case-study research was designed to revisit the question, “How do the pedagogical beliefs and classroom technology practices of teachers, recognized for their technology uses, align?”
This paper extends the unified theory of acceptance and use of technology (UTAUT) to study acceptance and use of technology in a consumer context. Our proposed UTAUT2 incorporates three constructs into UTAUT: hedonic motivation, price value, and habit. Individual differences — namely, age, gender, and experience — are hypothesized to moderate the effects of these constructs on behavioral intention and technology use. Results from a two-stage online survey, with technology use data collected four months after the first survey, of 1,512 mobile Internet consumers supported our model. Compared to UTAUT, the extensions proposed in UTAUT2 produced a substantial improvement in the variance explained in behavioral intention (56 percent to 74 percent) and technology use (40 percent to 52 percent). The theoretical and managerial implications of these results are discussed.
Advancements in artificial intelligence (AI) have stimulated the development of educational AI tools (EAIT). EAITs intelligently assist teachers in formulating better pedagogical decisions or actions for their students. However, teachers are hardly integrating EAITs, and little is known about their perceptions of EAITs. This study seeks to identify human factors that encourage or restrict teachers’ acceptance of EAITs. We propose a revised technology acceptance model incorporating teachers’ pedagogical beliefs and perceived trust in EAITs. Survey data were collected from 215 teachers in South Korea and analyzed using structural equation modeling. The results indicate that teachers with constructivist beliefs are more likely to integrate EAITs than teachers with transmissive orientations. Furthermore, perceived usefulness, perceived ease of use, and perceived trust in EAITs are determinants to be considered when explaining teachers’ acceptance of EAITs. Among them, the most influential determinant of predicting their acceptance was found to be how easily the EAIT is constructed. Significant implications for researchers and stakeholders regarding the development and integration of EAITs are discussed.
This article presents and briefly discusses some results of a survey conducted as part of a study on multiple literacies and the use of technology in English as a Foreign Language (EFL) classroom. An online questionnaire has been sent to English teachers of the third cycle and secondary education in Portugal with the aim to investigate their perceptions on the use of new technologies, particularly Web 2.0 tools, in their teaching practice. This article focuses particularly on the results from the questions related to materials and digital tools frequently used, teachers’ general view on the use of technology, as well as the digital and critical literacies approach. It has been found that although technology seems to be part of the teaching practice of this group of teachers – and although further research is necessary to deeply understand the actual use of technology in this particular scenario – it can be assumed that suitable guidance, training and further development of appropriate materials for teachers and students are necessary to facilitate and better integrate new technologies in the EFL classroom. Keywords: New technologies and language learning. Multiliteracies. Digital literacies. Critical thinking. English language teaching. RESUMO Este artigo apresenta e discute brevemente alguns resultados de uma pesquisa realizada como parte de um estudo sobre múltiplas literacias e o uso da tecnologia na aula de inglês como língua estrangeira (English as a Foreign Language – EFL) (Cardoso, 2017). Um questionário online foi enviado a professores de inglês do terceiro ciclo e secundário, em Portugal, com o objetivo de investigar as perceções e as opiniões deles a respeito do uso das novas tecnologias, especialmente dos recursos da Web 2.0, em sua prática profissional. O presente artigo enfoca particularmente os resultados obtidos das perguntas relacionadas à frequência de uso de materiais e recursos, à visão dos professores sobre o uso da tecnologia, assim como à abordagem das literacias digitais e críticas. Embora a tecnologia pareça fazer parte da prática discente desse grupo de professores, e ainda que pesquisas adicionais sejam necessárias para entender melhor o uso real dessa tecnologia nesse cenário em particular, é possível dizer que são necessárias algumas medidas para que a integração significativa e eficaz das novas tecnologias nas salas de aula de EFL, tais como, orientações adequadas e treinamento aos professores, e maior desenvolvimento de materiais apropriados. Palavras-chave: Novas tecnologias e ensino de línguas. Multiliteracias. Literaturas digitais. Pensamento crítico. Ensino de língua inglesa. RESUMEN Este artículo presenta y discute brevemente algunos resultados de una investigación realizada como parte de un estudio sobre múltiples literacias y el uso de la tecnología en la clase de inglés como lengua extranjera (Card., 2017). Un cuestionario en línea fue enviado a los profesores de Inglés Graduado de secundaria y, en Portugal, con el fin de investigar las percepciones y sus opiniones sobre el uso de las nuevas tecnologías, especialmente capacidades Web 2.0 en su práctica profesional. El presente artículo se centra particularmente en los resultados obtenidos de las preguntas relativas a la frecuencia de uso de materiales y recursos, a la visión de los profesores sobre el uso de la tecnología, así como al abordaje de las literas digitales y críticas. Aunque la tecnología parece formar parte de la práctica discente de este grupo de profesores, y aún si son necesarias investigaciones adicionales para entender mejor el uso real de esta tecnología en este escenario en particular, es posible decir que son necesarias algunas medidas para que la integración significativa y eficaz de las mismas nuevas tecnologías en las aulas de EFL, tales como orientación adecuada y capacitación a los profesores, y el desarrollo de materiales apropiados. Palabras clave: Nuevas tecnologías y enseñanza de lenguas. Multilenuales. Literaturas digitales. Pensamiento crítico. Enseñanza de lengua inglesa. DOI: http://dx.doi.org/10.22169/revint.v14i31.1523
Theoretical work is essential to the progress of any discipline. Theories, models, and frameworks are underdetermined representations of a phenomenon that help us understand and take action in a domain. However, the field of learning design and technology (LDT) has traditionally struggled with developing a solid theoretical foundation that is useful for both research and practice. We propose viewing theory building as an act of design might address these challenges. After defining key constructs and describing two approaches to theory development, we describe three design perspectives that might be useful for theory development: Lawson and Dorst’s (Design expertise, Elsevier, Amsterdam, 2009) view of design as a combination of analytical (problem-based) and creative (solution-based) moves, Schön’s (The reflective practitioner: how professionals think in action, Basic Books, New York, 1983) reflection-in-action, and design as dialogic interpretation (Snodgrass and Coyne in Des Issues 9(1):56–74, https://doi.org/10.2307/1511599, 1992). We use a case study to illustrate each perspective. We conclude with implications of a design approach to theory creation, including how design perspectives enable scholars to design possible futures.
Artificial intelligence (AI) is becoming increasingly integrated in user-facing technology, but public understanding of these technologies is often limited. There is a need for additional HCI research investigating a) what competencies users need in order to effectively interact with and critically evaluate AI and b) how to design learner-centered AI technologies that foster increased user understanding of AI. This paper takes a step towards realizing both of these goals by providing a concrete definition of AI literacy based on existing research. We synthesize a variety of interdisciplinary literature into a set of core competencies of AI literacy and suggest several design considerations to support AI developers and educators in creating learner-centered AI. These competencies and design considerations are organized in a conceptual framework thematically derived from the literature. This paper's contributions can be used to start a conversation about and guide future research on AI literacy within the HCI community.
This chapter draws a picture of the variety of representations that have been proposed to support the learning design life cycle. The intent is to show that such representations have different features and serve different purposes and that designers may find it useful to adopt one or the other according to their objectives and/or at different stages of their work. The argument is sustained throughout this chapter based on an example, concerning a learning activity, which is represented through several types of representations. The conclusion is that the quest for a single representation serving all purposes is vain, while the efforts of researchers should better be directed toward the aim of building tools that allow for interoperability of these representations and integration of the tools that make use of them, so to facilitate sharing and reuse of the half-fabricates of the learning design life cycle, as well as implementation of existing designs in different virtual learning environments (VLEs).
In this chapter, we introduce a framework, called technological pedagogical content knowledge (or TPACK for short), that describes the kinds of knowledge needed by a teacher for effective technology integration. The TPACK framework emphasizes how the connections among teachers’ understanding of content, pedagogy, and technology interact with one another to produce effective teaching. Even as a relatively new framework, the TPACK framework has significantly influenced theory, research, and practice in teacher education and teacher professional development. In this chapter, we describe the theoretical underpinnings of the framework, and explain the relationship between TPACK and related constructs in the educational technology literature. We outline the various approaches teacher educators have used to develop TPACK in pre- and in-service teachers, and the theoretical and practical issues that these professional development efforts have illuminated. We then review the widely varying approaches to measuring TPACK, with an emphasis on the interaction between form and function of the assessment, and resulting reliability and validity outcomes for the various approaches. We conclude with a summary of the key theoretical, pedagogical, and methodological issues related to TPACK, and suggest future directions for researchers, practitioners, and teacher educators.
The purpose of this exploratory mixed methods study was to investigate how teacher beliefs were related to technology integration practices. We were interested in how and to what extent teachers' (a) beliefs about the nature of knowledge and learning, (b) beliefs about effective ways of teaching, and (c) technology integration practices were related to each other. The participants were twenty two teachers who have participated in a four-year professional development project funded by the U.S. Department of Education. Specific relations between teachers' beliefs and technology integration practices are presented. The implications for professional development and suggestions for teacher belief change and technology integration are discussed.
This paper describes a framework for teacher knowledge for technology integration called technological pedagogical content knowledge (originally TPCK, now known as TPACK, or technology, pedagogy, and content knowledge). This framework builds on Lee Shulman's construct of pedagogical content knowledge (PCK) to include technology knowledge. The development of TPACK by teachers is critical to effective teaching with technology. The paper begins with a brief introduction to the complex, ill- structured nature of teaching. The nature of technologies (both analog and digital) is considered, as well as how the inclusion of technology in pedagogy further complicates teaching. The TPACK framework for teacher knowledge is described in detail, as a complex interaction among three bodies of knowledge: Content, pedagogy, and technology. The interaction of these bodies of knowledge, both theoretically and in practice, produces the types of flexible knowledge needed to successfully integrate technology use into teaching.
This paper presents the IPS-I-model: a model that describes the process of information problem solving (IPS) in which the Internet (I) is used to search information. The IPS-I-model is based on three studies, in which students in secondary and (post) higher education were asked to solve information problems, while thinking aloud. In-depth analyses of the thinking-aloud protocols revealed that the IPS-process consists of five constituent skills: (a) defining information problem, (b) searching information, (c) scanning information, (d) processing information, and (e) organizing and presenting information. Further, the studies revealed that regulation skills prove to be crucial for the on-going IPS-process. The IPS-I-model depicts the constituent skills, regulation skills, and important conditional skills. The model gives an initial impetus for designing IPS-instruction.
The AI revolution in education: Will AI replace or assist teachers in higher education
- C K Y Chan
- L H Y Tsi
C. K. Y. Chan and L. H. Y. Tsi, "The AI revolution in education: Will
AI replace or assist teachers in higher education?" arXiv:2305.01185,
pp. 1-18, 2023.
Good research practice
- S Stafström
S. Stafström, "Good research practice," 2017. [Online]. Available:
https://www.vr.se/english/analysis/reports/our-reports/2017-08-31-goo
d-research-practice.html