Computers in Human Behavior Reports

Artificial Intelligence competencies will become increasingly important in the near future. Therefore, it is essential that the AI literacy of individuals can be assessed in a valid and reliable way. This study presents the development of the “Scale for the assessment of non-experts' AI literacy” (SNAIL). An existing AI literacy item set was distributed as an online questionnaire to a heterogeneous group of non-experts (i.e., individuals without a formal AI or computer science education). Based on the data collected, an exploratory factor analysis was conducted to investigate the underlying latent factor structure. The results indicated that a three-factor model had the best model fit. The individual factors reflected AI competencies in the areas of “Technical Understanding”, “Critical Appraisal”, and “Practical Application”. In addition, eight items from the original questionnaire were deleted based on high intercorrelations and low communalities to reduce the length of the questionnaire. The final SNAIL-questionnaire consists of 31 items that can be used to assess the AI literacy of individual non-experts or specific groups and is also designed to enable the evaluation of AI literacy courses’ teaching effectiveness.

Matthias Carl Laupichler

Alexandra Aster

Nicolas Haverkamp

Tobias Raupach

Development of the “Scale for the assessment of non-experts’ AI literacy” – An exploratory factor analysis

“非专家人工智能素养评估量表”的开发——探索性因子分析

Automation may have varied consequences on individual workers, ranging from job loss to enhanced productivity. Although some of these disruptions may be outside of their control, employees' perceptions and reactions to automation may also play a part in shaping automation's impact on them. To this end, we draw on transactional theory of stress to distinguish between a variety of automation perceptions. In particular, we suggest that employees' beliefs that technology can conduct their work (perceived automatability) will be related to both pessimistic (automation-related job insecurity) and optimistic (automation-related performance optimism) appraisals regarding how automation will impact job prospects. Reasoning that people who feel in control feel they can mitigate the harms and harness the potential benefits of automation, we propose that perceived control moderates the extent to which perceived automatability leads to job insecurity and performance optimism. In-turn, we expect that automation-related job insecurity and performance optimism will have contrasting effects on job engagement and turnover intentions. Using a survey (N = 500) and an experiment (N = 194) we found overall support for these predictions. As such, this research sheds new light on employees' perspectives on automation at work, with implications for organizations and policymakers seeking to help employees transition to the future of work.

Anna F. Gödöllei

James W. Beck

Insecure or optimistic? Employees’ diverging appraisals of automation, and consequences for job attitudes

不安全还是乐观？员工对自动化的不同评价及其对工作态度的影响

Maik Beege

Christopher Hug

Josef Nerb

AI in STEM Education: The Relationship between Teacher Perceptions and ChatGPT Use

STEM教育中的人工智能：教师认知与ChatGPT使用的关系

With the advancement of technology, emerging technologies like Artificial Intelligence (AI) have also been growing rapidly and becoming more common than ever before. Kenya has taken tremendous steps in adopting the use of emerging technology in different sectors of the economy. In realization of the need to have a skilled digital workforce to develop solutions using these emerging technologies, Kenya has undertaken curriculum reforms and introduced the Competency-Based Curriculum (CBC) which has included digital literacy and coding in elementary school. Furthermore, computer science has been introduced in Junior Secondary School. In view of these changes, teachers should be adequately prepared with knowledge, skills, and attitudes to effectively teach these new technologies. However, in Kenya, AI was not and still is not part of the teacher training curriculum. Additionally, there are inadequate professional development opportunities in AI for both pre-service and in-service teachers since AI is not part of the CBC curriculum. That notwithstanding, it is inevitable for teachers in the current world to introduce AI to learners. Therefore, this study's objectives were to assess the confidence in AI, attitudes toward AI, AI ethics, subjective norms, perceived threats, and the readiness to teach AI among Kenyan K-12 in-service teachers and to assess how these factors influence their readiness to teach AI. To achieve these objectives, this study employed a quantitative research methodology by administering a survey using Google Forms to a random sample of 308 teachers from different grades from 37 out of 47 counties in Kenya. The findings showed that confidence in AI, AI ethics and subjective norms significantly influenced AI readiness while attitude towards AI and perceived threats did not significantly influence AI readiness. These results are significant in providing a basis for education policy change on AI education in Kenya, such as transforming the teacher training curriculum to include AI and designing AI professional development programs for in-service teachers to ensure they are well-equipped to teach AI.

Maxwell Fundi

Ismaila Temitayo Sanusi

Solomon Sunday Oyelere

Mildred Ayere

Advancing AI education: Assessing Kenyan in-service teachers' preparedness for integrating artificial intelligence in competence-based curriculum

推进人工智能教育：评估肯尼亚在职教师在能力本位课程中整合人工智能的准备情况

A new General Attitudes towards Artificial Intelligence Scale (GAAIS) was developed. The scale underwent initial statistical validation via Exploratory Factor Analysis, which identified positive and negative subscales. Both subscales captured emotions in line with their valence. In addition, the positive subscale reflected societal and personal utility, whereas the negative subscale reflected concerns. The scale showed good psychometric indices and convergent and discriminant validity against existing measures. To cross-validate general attitudes with attitudes towards specific instances of AI applications, summaries of tasks accomplished by specific applications of Artificial Intelligence were sourced from newspaper articles. These were rated for comfortableness and perceived capability. Comfortableness with specific applications was a strong predictor of general attitudes as measured by the GAAIS, but perceived capability was a weaker predictor. Participants viewed AI applications involving big data (e.g. astronomy, law, pharmacology) positively, but viewed applications for tasks involving human judgement, (e.g. medical treatment, psychological counselling) negatively. Applications with a strong ethical dimension led to stronger discomfort than their rated capabilities would predict. The survey data suggested that people held mixed views of AI. The initially validated two-factor GAAIS to measure General Attitudes towards Artificial Intelligence is included in the Appendix.

Astrid Schepman

Paul Rodway

Initial validation of the general attitudes towards Artificial Intelligence Scale

人工智能通用态度量表的初步验证

Ted C.T. Fong

Edmond P.H. Choi

Daniel Y.T. Fong

Man Chun Kung

Mona W.C. Lam

Antoinette M. Lee

William C.W. Wong

Tai Hing Lam

Paul S.F. Yip

Exploring patterns of online sexual experiences and associated factors in 7,901 high school students in Hong Kong: A latent class analysis

探索香港7,901名高中生在线性经历模式及其相关因素：潜在类别分析

Dragoș M. Obreja

Răzvan Rughiniș

Daniel Rosner

Mapping the multidimensional trend of generative AI: A bibliometric analysis and qualitative thematic review

生成式人工智能的多维趋势映射：文献计量分析与定性主题综述

Sergio C. Torres

Does digital grabbing boost affectivity in less empathic users? An interactive approach to affective laterality: Empathy, hand dominance, and action-context shape pleasantness experience

数字抓取是否增强低共情用户的情感体验？一种互动式情感偏侧化方法：共情、手部优势与动作情境塑造愉悦感

Sarah C. Cregan

Adam J. Toth

Mark J. Campbell

Comparing the play of sport and action-adventure game genres on heart rate and heart rate variability

比较体育与动作冒险游戏类型对心率和心率变异性的影响

Alaa Aladini

Sayed M. Ismail

Mohamad Ahmad Saleem Khasawneh

Goodarz Shakibaei

Self-directed writing development across computer/AI-based tasks: Unraveling the traces on L2 writing outcomes, growth mindfulness, and grammatical knowledge

基于计算机/人工智能任务的自主写作发展：揭示对二语写作成果、成长意识和语法知识的影响

Abbreviation	Comput. Hum. Behav. Rep.
Journal Impact	4.99
Quartiles(Global)	PSYCHOLOGY, MULTIDISCIPLINARY(Q1)

ISSN	2451-9588
h-index	22

Publication Information	Publisher: Elsevier B.V.，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2020，Proportion of original research papers: ，Self Citation Rate:， Gold OA Rate:
Journal Fees	$1580Details
Average review cycle	22 weeks

Computers in Human Behavior Reports

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