Amer, M., Daim, T., & Jetter, A. (2013). A review of scenario planning. Futures, 46, 23–40.
Atanassova, I., Bertin, M., & Mayr, P. (2019). Editorial: mining scientific papers: NLP-enhanced bibliometrics. Frontiers in Research Metrics and Analytics. https://doi.org/10.3389/frma.2019.00002.
Auger, J. (2013). Speculative design: Crafting the speculation. Digital Creativity, 24(1), 11–35.
Badampudi, D., Wohlin, C., & Petersen, K. (2015). Experiences from using snowballing and database searches in systematic literature studies. In Proceedings of the 19th International Conference on Evaluation and Assessment in Software Engineering (pp. 1–10).
Baker, T., Smith, L. and Anissa, N. (2019). Educ-AI-tion Rebooted? Exploring the future of artificial intelligence in schools and colleges. NESTA. https://www.nesta.org.uk/report/education-rebooted/.
Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020). Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education. https://doi.org/10.1186/s41239-020-00218-x.
Bayne, S. (2015). Teacherbot: interventions in automated teaching. Teaching in Higher Education, 20(4), 455–467.
Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., & Tanaka, F. (2018). Social robots for education: A review. https://doi.org/10.1126/scirobotics.aat5954.
Blanchard, E. G. (2015). Socio-cultural imbalances in AIED research: Investigations, implications and opportunities. International Journal of Artificial Intelligence in Education, 25(2), 204–228.
Bleecker, J. (2009). Design fiction: A short essay on design, science, fact and fiction. Near Future Lab.
Blythe, M. (2017). Research fiction: storytelling, plot and design. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 5400–5411).
Blythe, M., Andersen, K., Clarke, R., & Wright, P. (2016). Anti-solutionist strategies: Seriously silly design fiction. Conference on Human Factors in Computing Systems - Proceedings (pp. 4968–4978). Association for Computing Machinery.
Brevini, B. (2020). Black boxes, not green: Mythologizing artificial intelligence and omitting the environment. Big Data & Society, 7(2), 2053951720935141.
Canzonetta, J., & Kannan, V. (2016). Globalizing plagiarism & writing assessment: a case study of Turnitin. The Journal of Writing Assessment, 9(2). http://journalofwritingassessment.org/article.php?article=104.
Carroll, J. M. (1999) Five reasons for scenario-based design. In Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. HICSS-32. Abstracts and CD-ROM of Full Papers, Maui, HI, USA, 1999, pp. 11. https://doi.org/10.1109/HICSS.1999.772890.
Catlin, D., Kandlhofer, M., & Holmquist, S. (2018). EduRobot Taxonomy a provisional schema for classifying educational robots. 9th International Conference on Robotics in Education 2018, Qwara, Malta.
Clay, J. (2018). The challenge of the intelligent library. Keynote at What does your eResources data really tell you? 27th February, CILIP.
Crawford, K., & Joler, V. (2018) Anatomy of an AI system, https://anatomyof.ai/.
Darby, E., Whicher, A., & Swiatek, A. (2017). Co-designing design fictions: a new approach for debating and priming future healthcare technologies and services. Archives of design research. Health Services Research, 30(2), 2.
Demartini, C., & Benussi, L. (2017). Do Web 4.0 and Industry 4.0 Imply Education X.0? IT Pro, 4–7.
Dong, Z. Y., Zhang, Y., Yip, C., Swift, S., & Beswick, K. (2020). Smart campus: Definition, framework, technologies, and services. IET Smart Cities, 2(1), 43–54.
Dourish, P., & Bell, G. (2014). “resistance is futile”: Reading science fiction alongside ubiquitous computing. Personal and Ubiquitous Computing, 18(4), 769–778.
Dunne, A., & Raby, F. (2001). Design noir: The secret life of electronic objects. New York: Springer Science & Business Media.
Fjeld, J., Achten, N., Hilligoss, H., Nagy, A., & Srikumar, M. (2020). Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for AI. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3518482.
Følstad, A., Skjuve, M., & Brandtzaeg, P. (2019). Different chatbots for different purposes: Towards a typology of chatbots to understand interaction design. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11551 LNCS, pp. 145–156. Springer Verlag.
Future TDM. (2016). Baseline report of policies and barriers of TDM in Europe. https://project.futuretdm.eu/wp-content/uploads/2017/05/FutureTDM_D3.3-Baseline-Report-of-Policies-and-Barriers-of-TDM-in-Europe.pdf.
Gabriel, A. (2019). Artificial intelligence in scholarly communications: An elsevier case study. Information Services & Use, 39(4), 319–333.
Griffiths, D. (2015). Visions of the future, horizon report. LACE project. http://www.laceproject.eu/visions-of-the-future-of-learning-analytics/.
Heaven, D. (2018). The age of AI peer reviews. Nature, 563, 609–610.
Hockly, N. (2019). Automated writing evaluation. ELT Journal, 73(1), 82–88.
Holmes, W., Bialik, M. and Fadel, C. (2019). Artificial Intelligence in Education. The center for curriculum redesign. Boston, MA.
Hussein, M., Hassan, H., & Nassef, M. (2019). Automated language essay scoring systems: A literature review. PeerJ Computer Science. https://doi.org/10.7717/peerj-cs.208.
Inayatullah, S. (2008). Six pillars: Futures thinking for transforming. foresight, 10(1), 4–21.
Jarke, J., & Breiter, A. (2019). Editorial: the datafication of education. Learning, Media and Technology, 44(1), 1–6.
JISC. (2019). The intelligent campus guide. Using data to make smarter use of your university or college estate. https://www.jisc.ac.uk/rd/projects/intelligent-campus.
Jones, E., Kalantery, N., & Glover, B. (2019). Research 4.0 Interim Report. Demos.
Jones, K. (2019). “Just because you can doesn’t mean you should”: Practitioner perceptions of learning analytics ethics. Portal, 19(3), 407–428.
Jones, K., Asher, A., Goben, A., Perry, M., Salo, D., Briney, K., & Robertshaw, M. (2020). “We’re being tracked at all times”: Student perspectives of their privacy in relation to learning analytics in higher education. Journal of the Association for Information Science and Technology. https://doi.org/10.1002/asi.24358.
King, R. D., Rowland, J., Oliver, S. G., Young, M., Aubrey, W., Byrne, E., et al. (2009). The automation of science. Science, 324(5923), 85–89.
Kitano, H. (2016). Artificial intelligence to win the nobel prize and beyond: Creating the engine for scientific discovery. AI Magazine, 37(1), 39–49.
Kwet, M., & Prinsloo, P. (2020). The ‘smart’ classroom: a new frontier in the age of the smart university. Teaching in Higher Education, 25(4), 510–526.
Lacity, M., Scheepers, R., Willcocks, L. & Craig, A. (2017). Reimagining the University at Deakin: An IBM Watson Automation Journey. The Outsourcing Unit Working Research Paper Series.
Lowendahl, J.-M., & Williams, K. (2018). 5 Best Practices for Artificial Intelligence in Higher Education. Gartner. Research note.
Luckin, R. (2017). Towards artificial intelligence-based assessment systems. Nature Human Behaviour, 1(3), 1–3.
Luckin, R., & Holmes, W. (2017). A.I. is the new T.A. in the classroom. https://howwegettonext.com/a-i-is-the-new-t-a-in-the-classroom-dedbe5b99e9e.
Luckin, R., Holmes, W., Griffiths, M., & Pearson, L. (2016). Intelligence unleashed an argument for AI in Education. Pearson. https://www.pearson.com/content/dam/one-dot-com/one-dot-com/global/Files/about-pearson/innovation/open-ideas/Intelligence-Unleashed-v15-Web.pdf.
Lyckvi, S., Wu, Y., Huusko, M., & Roto, V. (2018). Eagons, exoskeletons and ecologies: On expressing and embodying fictions as workshop tasks. ACM International Conference Proceeding Series (pp. 754–770). Association for Computing Machinery.
Macgilchrist, F. (2019). Cruel optimism in edtech: When the digital data practices of educational technology providers inadvertently hinder educational equity. Learning, Media and Technology, 44(1), 77–86.
Manolev, J., Sullivan, A., & Slee, R. (2019). The datafication of discipline: ClassDojo, surveillance and a performative classroom culture. Learning, Media and Technology, 44(1), 36–51.
Martha, A. S. D., & Santoso, H. B. (2019). The design and impact of the pedagogical agent: A systematic literature review. Journal of Educators Online, 16(1), n1.
Maughan, T. (2016). The hidden network that keeps the world running. https://datasociety.net/library/the-hidden-network-that-keeps-the-world-running/.
McDonald, D., & Kelly, U. (2012). The value and benefits of text mining. England: HEFCE.
Min-Allah, N., & Alrashed, S. (2020). Smart campus—A sketch. Sustainable Cities and Society. https://doi.org/10.1016/j.scs.2020.102231.
Nathan, L. P., Klasnja, P. V., & Friedman, B. (2007). Value scenarios: a technique for envisioning systemic effects of new technologies. In CHI'07 extended abstracts on human factors in computing systems (pp. 2585–2590).
Nurshatayeva, A., Page, L. C., White, C. C., & Gehlbach, H. (2020). Proactive student support using artificially intelligent conversational chatbots: The importance of targeting the technology. EdWorking paper, Annenberg University https://www.edworkingpapers.com/sites/default/files/ai20-208.pdf.
Page, L., & Gehlbach, H. (2017). How an artificially intelligent virtual assistant helps students navigate the road to college. AERA Open. https://doi.org/10.1177/2332858417749220.
Pinkwart, N. (2016). Another 25 years of AIED? Challenges and opportunities for intelligent educational technologies of the future. International journal of artificial intelligence in education, 26(2), 771–783.
Price, S., & Flach, P. (2017). Computational support for academic peer review: A perspective from artificial intelligence. Communications of the ACM, 60(3), 70–79.
Rapp, A. (2020). Design fictions for learning: A method for supporting students in reflecting on technology in human–computer interaction courses. Computers & Education, 145, 103725.
Reid, P. (2014). Categories for barriers to adoption of instructional technologies. Education and Information Technologies, 19(2), 383–407.
Renz, A., & Hilbig, R. (2020). Prerequisites for artificial intelligence in further education: Identification of drivers, barriers, and business models of educational technology companies. International Journal of Educational Technology in Higher Education. https://doi.org/10.1186/s41239-020-00193-3.
Roll, I., & Wylie, R. (2016). Evolution and Revolution in Artificial Intelligence in Education. International Journal of Artificial Intelligence in Education, 26(2), 582–599.
Rummel, N., Walker, E., & Aleven, V. (2016). Different futures of adaptive collaborative learning support. International Journal of Artificial Intelligence in Education, 26(2), 784–795.
Schoenenberger, H. (2019). Preface. In H. Schoenenberger (Ed.), Lithium-ion batteries a machine-generated summary of current research (v–xxiii). Berlin: Springer.
Selwyn, N. (2019a). Should robots replace teachers? AI and the future of education. New Jersey: Wiley.
Selwyn, N. (2019b). What’s the problem with learning analytics? Journal of Learning Analytics, 6(3), 11–19.
Selwyn, N., Pangrazio, L., Nemorin, S., & Perrotta, C. (2020). What might the school of 2030 be like? An exercise in social science fiction. Learning, Media and Technology, 45(1), 90–106.
Sparkes, A., Aubrey, W., Byrne, E., Clare, A., Khan, M. N., Liakata, M., et al. (2010). Towards robot scientists for autonomous scientific discovery. Automated Experimentation, 2(1), 1.
Strobl, C., Ailhaud, E., Benetos, K., Devitt, A., Kruse, O., Proske, A., & Rapp, C. (2019). Digital support for academic writing: A review of technologies and pedagogies. Computers and Education, 131, 33–48.
Templier, M., & Paré, G. (2015). A framework for guiding and evaluating literature reviews. Communications of the Association for Information Systems, 37(1), 6.
Thelwall, M. (2019). Artificial intelligence, automation and peer review. Bristol: JISC.
Tsai, Y., & Gasevic, D. (2017). Learning analytics in higher education—Challenges and policies: A review of eight learning analytics policies. ACM International Conference Proceeding Series (pp. 233–242). Association for Computing Machinery.
Tsai, Y. S., Poquet, O., Gašević, D., Dawson, S., & Pardo, A. (2019). Complexity leadership in learning analytics: Drivers, challenges and opportunities. British Journal of Educational Technology, 50(6), 2839–2854.
Tsekleves, E., Darby, A., Whicher, A., & Swiatek, P. (2017). Co-designing design fictions: A new approach for debating and priming future healthcare technologies and services. Archives of Design Research, 30(2), 5–21.
Wellnhammer, N., Dolata, M., Steigler, S., & Schwabe, G. (2020). Studying with the help of digital tutors: Design aspects of conversational agents that influence the learning process. Proceedings of the 53rd Hawaii International Conference on System Sciences, (pp. 146–155).
Williamson, B. (2019). Policy networks, performance metrics and platform markets: Charting the expanding data infrastructure of higher education. British Journal of Educational Technology, 50(6), 2794–2809.
Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology. https://doi.org/10.1080/17439884.2020.1798995.
Wilsdon, J. (2015). The metric tide: Independent review of the role of metrics in research assessment and management. Sage.
Winkler, R. & Söllner, M. (2018). Unleashing the potential of chatbots in education: A state-of-the-art analysis. In: Academy of Management Annual Meeting (AOM). Chicago, USA.
Woolf, B. P., Lane, H. C., Chaudhri, V. K., & Kolodner, J. L. (2013). AI grand challenges for education. AI Magazine, 34(4), 66–84.
Zawacki-Richter, O., Marín, V., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—where are the educators? International Journal of Educational Technology in Higher Education. https://doi.org/10.1186/s41239-019-0171-0.
Zeide, E. (2017). The structural consequences of big data-driven education. Big Data, 5(2), 164–172.