Empowering Qualitative Research Methods in Education with Artificial Intelligence

Artificial Intelligence is one of the fastest growing disciplines, disrupting many sectors. Originally mainly for computer scientists and engineers, it has been expanding its horizons and empowering many other disciplines contributing to the development of many novel applications in many sectors. These include medicine and health care, business and finance, psychology and neuroscience, physics and biology to mention a few. However, one of the disciplines in which artificial intelligence has not been fully explored and exploited yet is education. In this discipline, many research methods are employed by scholars, lecturers and practitioners to investigate the impact of different instructional approaches on learning and to understand the ways skills and knowledge are acquired by learners. One of these is qualitative research, a scientific method grounded in observations that manipulates and analyses non-numerical data. It focuses on seeking answers to why and how a particular observed phenomenon occurs rather than on its occurrences. This study aims to explore and discuss the impact of artificial intelligence on qualitative research methods. In particular, it focuses on how artificial intelligence have empowered qualitative research methods so far, and how it can be used in education for enhancing teaching and learning

Downregulation of <i>IRAIN</i> long non-coding RNA predicts unfavourable clinical outcome in acute myeloid leukaemia patients

Although it has been shown that the long non-coding RNA (lncRNA) insulin-like growth factor type 1 receptor (IGF1R) antisense imprinted non-protein coding RNA (IRAIN) is downregulated in leukaemia cell lines, its usefulness as a prognostic marker in acute myeloid leukaemia (AML) has not yet been thoroughly investigated. Here, we sought to determine whether the expression of IRAIN is associated with clinical outcome of AML patients. Using quantitative real-time polymerase chain reaction (qRT-PCR), IRAIN expression levels were assessed in peripheral blood leukocyte samples from 150 patients with AML and 50 healthy controls. Analysis was done on the relationship between IRAIN expression and clinical outcomes in AML patients. When compared to healthy controls, IRAIN expression was markedly reduced in AML patients (P = 0.019). IRAIN expression could distinguish French-American-British (FAB) subtypes of AML (P = 0.024). Low IRAIN expression status was associated with shorter event-free survival (EFS) in the non-t(15;17) cytogenetically abnormal AML subset (P = 0.004). IRAIN downregulation was identified as an independent adverse prognostic marker for complete remission (CR) not only in the in the non-t(15;17) cytogenetically abnormal AML subset (P = 0.006) but also in the AML-M4/M5 subgroup (P = 0.033). Aberrantly low IRAIN expression is closely associated with lower CR rates in AML patients, particularly in non-t(15;17) cytogenetically abnormal AML and M4/M5 AML, suggesting that the determination of IRAIN expression level at diagnosis provides valuable prognostic information, serves as a promising biomarker for evaluating treatment response, and helps predicting clinical outcome of AML patients.</p