Since ChatGPT-3.5 was released in late 2022, educators and higher education institutions have wrestled with the potential risks that generative artificial intelligence (GenAI) poses to assessment validity. It’s encouraging that, as a sector, we’ve invested significant energy and resources into understanding these challenges and identifying strategies to uphold academic integrity – an essential element of reputation of our universities. But despite the efforts, have we got a clear path forward?
A few weeks ago, an interesting debate unfolded on LinkedIn about whether degrees completed online should have ‘online’ written on the certificate. The idea behind it seemed to be about distinguishing online and face-to-face (F2F) degrees—apparently so employers could decide which ones they could trust more when hiring. The discussion originally stemmed from a closed-door dialogue held among some higher education leaders in Australia. Funny enough, it reminded me of a similar debate I was part of a decade ago in the Maldives.
In an article titled, Cheating has become normal, Beth McMurtrie highlighted the growing prevalence of academic misconduct in U.S. higher education. The statistics were alarming: in 2024, 65% of students admitted to some form of cheating—a staggering 30% increase compared to 2019. While these figures were based on self-reported data from a single university and don’t necessarily reflect the broader higher education landscape, they spotlight a significant issue – cheating in higher education. Cheating has become a recurring topic of concern among educators, especially since the emergence of Generative AI tools like ChatGPT in 2022. In response, many educators have called for a redesign of assessment. But this raises the question: can we really design out cheating?
Over the past two years, educators, AI experts, and university leaders have come together in countless webinars, seminars, workshops, panel discussions, and conferences to explore the potential effects of AI on education. A large portion of these efforts has focused on the risks to academic and research integrity and how we might navigate them. Like many enthusiasts of digital technology and pedagogy, I joined numerous discussions and events to understand these issues better. It was heartening to see the genuine care we collectively hold for learning and teaching. Through these discussions, we’ve indeed addressed the elephant in the room. But have we truly taken the next steps? I’m not convinced!
A few weeks ago, I had the privilege of facilitating a professional learning workshop on group assessment design for a group of academics at the University of Wollongong. It was inspiring to see how passionate and enthusiastic our educators are about enhancing student learning, particularly in the design and implementation of assessments. Our discussion couldn’t have been more timely, given the growing concerns about GenAI’s impact on academic integrity and the validity of the assessments. The insights shared by these dedicated individuals have inspired me to write this post.
Imagine that you came across a massive sale at a shopping centre and feeling excited to buy a pair of shoes from a footwear shop. However, upon entering, you realise that the shop offers only one type, one colour, and one size of shoes. There is no way to choose a pair based on your age, foot shape, size, design, colour preferences, or gender. How does this sound to you? Does it sound like a desirable or bad experience? Also, do you think this scenario is comparable to the educational practice of designing learning experiences for the mythical average learner?
Many years ago, I was an eighteen-years-old preservice teacher – one of the many young women and men who were striving to lay their first foundation of learning and teaching. Yes, I am referring to my primary teacher training course where I learned the fundamentals of learning science. It was a life-changing experience in many ways, and indeed, a solid foundation for me to become who I am today. For that, I am eternally grateful to all my amazing educators. They helped me understand what it means to become a teacher, learning designer, and educator. Now, I’m no longer that skinny young boy. Neither am I a novice teacher who constantly thinks about how to engage his 10-12-year-old students in his classes. Over the years, I have been fortunate to gain much new knowledge and experience which have changed how I think about learning and teaching today. One such aspect is learning styles.
I recently came across this intriguing quote: ‘Education is not the filling of a pail, but the lighting of a fire.’ As I first read it, I sort of fell in love with it, and it made me reflect on my beliefs and practices of teaching and learning. I searched the internet found that the quote is often attributed to the famous Irish poet and Nobel Prize winner, William Butler Yeats. But I asked myself: what does it really mean? How does it relate to how we teach and learn today?
އޮސްޓްރޭލިޔާގައި އަހަރެން އުޅުނު ސްޓޫޑަންޓް އެކޮމަޑޭޝަނެކެވެ. އެއިމާރާތުގައި ފަހަތުގައި 3 ނުވަތަ 4 ހަނި މަގު/ގޯޅި އޮވެއެވެ. މިމަގުތަކަކީ ހަނި ނަމަވެސް، އެއްވެސް މިންވަރަކަށް ޓްރެފިކް ތޮއްޖެހޭ މަގުތަކެއް ނޫނެވެ. ނަމަވެސް އިމާރާތް ހުރި ސަރަހައްދުގެ ގޮތުން ހަފްތާ ބަންދު ފިޔަވައި އެހެން ދުވަސްތަކުގައި މިމަގުތަކުގައި ދުވާލުގަޑީގައި ޕާކުރެވެނީ 2 ގަޑިއިރަށެވެ. ކޮންމެއަކަސް، ހަފްތާ ދުވަސްތަކުގެ ތެރެއިން ދުވަހެއްގައި، ގެއާ ކައިރީގައި، މިއިން މަގެއްގައި އަހަރެންގެ ކާރު ޕާކުކޮށްފީމެވެ. މާބޮޑު ކަމެކޭ ހީކުރުމެއްނެތިއެވެ. ނަމަވެސް މެންދުރެއްހައިއިރު ބޭރަށް ނިކުތްއިރު ކަމަކު ދާކަށްނެތެވެ. ކާރުގައި ސްޓިކާއެއް ހުއްޓެވެ. ކަނޑައެޅިފައިވާ ވަގުތަށްވުރެ ގިނަ ވަގުތު އެމަގުގައި ޕާކު ކުރީތީވެ ޖުރިމާނާ ކޮށްފައެވެ. 120 ވަރަކަށް ޑޮލަރުންނެވެ.
ދާދި ފަހުން ދަ އިކޮނޮމިސްޓް ގައި “ވިއެޓްނާމްގެ އިސްކޫލުތައް އެހާ ރަނގަޅީ ކީއްވެ؟” މިސުރުޚީގެ ދަށުން ޝާއިއުކޮށްފައިވާ މަޒުމޫނެއްގައި ބުނެފައިވާ ގޮތުގައި ވިއެޓްނާމްގެ މަދަރުސީ ތައުލީމުގެ ނިޒާމަކީ ދުނިޔޭގެ އެންމެ ރަނގަޅު މިފަދަ އެއް ނިޒާމެވެ. ވާރލްޑް ބޭންކާ ހަވާލާދީ މިމަޒުމޫނުގައި ބުނެފައިވާގޮތުގައި ބައިނަލްއަގުވާމީ ފެންވަރުގެ އިމްތިހާނުތަކުން ކިޔުން (ރީޑިންގ)، ހިސާބު، އަދި ސައިންސް މިމާއްދާތަކުން ވިއެޓްނާމްގެ ދަރިވަރުން ހޯދާ ނަތީޖާ، ދުނިޔޭގެ ކުރިއަރާފައިވާ ބޮޑެތި ގައުމުތަކުގެތެރޭގައި ހިމެނޭ އިނގިރޭސިވިލާތާއި ކެނެޑާފަދަ ބައެއް ގައުމުތަކަށްވުރެ ރަނގަޅެވެ. ނަމަވެސް މިލިޔުމުން އަޅުގަނޑު އެންމެ އަޅައިގަތީ މިކަން މިހެން މެދުވެރިވާ ސަބަބު ބަޔާންކޮށްފައިވާ ޕެރެގުރާފުގައެވެ. އޭގައި ވެއެވެ. “އެންމެ ބޮޑު ސަބަބަކީ ވިއެޓްނާމްގެ މުދައްރިސުންގެ ފެންވަރުމަތި ކަމެވެ. އެމީހުންގެ މުދައްރިސްކަމުގެ ޝަރުޠު ފުރިހަމައަށް ހަމަވުން އެކަންޏެއް ނޫނެވެ. އެމީހުންގެ ކިޔަވައިދިނުންވެސް ހަމަ އާދަޔާޚިލާފަށް މޮޅީއެވެ.”
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