Zhang Yongce of Dalian University of Technology's "grassroots experiment" in AI chemistry teaching: self-developed AI titration recognition equipment, with competitions drawing over a hundred universities

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Author: Chen Bin Source: China Science Daily Published: 2026/11/3 7:39:52

Staring intently at the color change of the indicator with the naked eye and controlling the amount of solution added drop by drop—the "titration experiment" is a basic experiment that chemistry students cannot avoid, and it is also teaching content all too familiar to Zhang Yongce, a senior engineer at the School of Chemistry at Dalian University of Technology (hereinafter referred to as DUT). But he never expected that, with the arrival of the artificial intelligence (AI) era, starting from this experiment, his teaching path would embark on a different journey.

At an analytical chemistry annual conference in 2023, when Zhang Yongce heard that AI could replace human eyes to automatically capture color signals and fully automate titration, he, who had been rooted in experimental teaching for many years, felt a stir in his heart.

It was precisely this "stir" in his heart that led Zhang Yongce, over the following years, to launch a "grassroots experiment" combining AI with chemistry course teaching—he not only quickly turned that concept, which had remained in the mouths of experts, into a piece of experimental equipment, but also used this equipment to organize on-campus competitions, offer related courses, and even develop the competition into a series of events attracting students from more than 100 universities in 13 provinces across the country.

Some education experts said bluntly, "This is true teaching innovation."

Zhang Yongce (lower right) instructing students, with the AI-recognition titration experimental equipment he developed in the background. Photo courtesy of the interviewee

Where Are the Teachers

As a native "DUT person," Zhang Yongce studied chemical trade as an undergraduate and switched to physical chemistry for his master's degree. That was around the turn of the century, when domestic universities had just begun using electronic courseware. However, many teachers were not familiar with this "new gadget," yet Zhang Yongce developed a strong interest in it.

In 2005, Zhang Yongce graduated and stayed at the university, becoming a teacher at the School of Chemistry. For many years afterward, he maintained a strong interest in teaching-related technology. Coupled with his background in physics-related fields, Zhang Yongce became a rare interdisciplinary talent in chemistry circles who understood both experiments and computer knowledge.

Coincidentally, those years were also a "golden period" when a large number of new technologies entered classroom teaching. From virtual simulation technology to 3D printing, and then to the rise of AI, all of these gave Zhang Yongce opportunities and space to innovate in experimental courses.

However, as he conducted more and more experiments, he developed a sense of "crisis."

He once summarized the origin of this feeling with a question: "When AI for science becomes a new paradigm of scientific research, where are the teachers?"

"This question is very realistic." Zhang Yongce told China Science Daily that AI participation in chemistry experiments has currently become a frontier trend in the chemistry field, but such teaching projects are rarely involved in university chemistry courses. The consequence is that when students graduate and enter research groups, due to a lack of practice, their awareness of interdisciplinary issues is very weak, and they lack basic AI application capabilities.

Therefore, at that annual conference 3 years ago, when the guest on the podium spoke of the concept of "AI-recognition titration experiments," Zhang Yongce, sitting in the audience, suddenly felt that "this might be an opportunity."

From "Benefiting" to "Passing It On"

This story was told by Zhang Yongce not long ago at the national finals of the 11th National University Teaching Innovation Competition (hereinafter referred to as the XJTLU Teaching Innovation Competition) hosted by Xi'an Jiaotong-Liverpool University. In that competition, he stood out from more than 8,000 participating teachers and won a national first prize, relying on the set of AI-recognition titration experimental equipment he had "tinkered together."

In fact, this was not the first time Zhang Yongce had won an award in this competition.

As early as around 2018, based on the then newly emerging virtual simulation technology, Zhang Yongce led the development of an app that could conduct virtual chemistry experiments in a network environment—the "Mobile Virtual Laboratory" (hereinafter referred to as the "Virtual Laboratory")—and launched more than 400 experimental projects on it. This app also won him a second prize in the XJTLU Teaching Innovation Competition.

Liu Hongyun, a professor in the Department of Chemistry at Beijing Normal University, first met Zhang Yongce through that "Virtual Laboratory."

The time goes back to 2020. At that time, affected by the COVID-19 pandemic, students could not conduct offline chemistry experiments, yet chemistry teaching was precisely inseparable from experiments, which put Liu Hongyun, who taught undergraduate chemistry, in a difficult position. She began searching the entire internet for a virtual platform that could conduct online chemistry experiments, but found very little; even when there were suitable platforms, most responded to the requests for help she sent with "read but no reply."

Until she accidentally found Zhang Yongce's "Virtual Laboratory."

"The program Teacher Zhang designed met almost all my needs," Liu Hongyun said. More importantly, when she sent Zhang Yongce a request for help, the latter did not hesitate at all, readily sent over the account and password, and even told her: "Let all your students use it."

This incident touched Liu Hongyun deeply.

"It can be seen that Teacher Zhang did not care much about this matter at the time; one could even say that he designed, used, and even gave away this software with a 'playful' attitude, but it was precisely his casual gift that solved my urgent need at the time," she said.

However, a question followed—in domestic universities, chemistry is one of the most widely distributed disciplines, and the number of teachers engaged in chemistry teaching is enormous, so why is it that only Zhang Yongce could "play" out these "new gadgets"?

Speaking of this, Zhang Yongce is very grateful for his circumstances. "My situation is a bit special." He told China Science Daily that he holds a full-time experimental teaching position, whose responsibility is "teaching students to do experiments," and such a position setup is rare among domestic universities.

"I am quite grateful for the school's support for experimental positions—we just need to focus on teaching students experiment courses well, which has given me a lot of space to ponder experimental details. Moreover, DUT's engineering chemistry has always had an excellent teaching tradition; I myself am a beneficiary, and I am very willing to continue passing it on," Zhang Yongce said. In addition, the school's strong engineering foundation makes it easy for him to consult "experts" in relevant fields when he encounters some engineering problems.

This point deeply struck Zhang Xiaojun, one of the judges of the XJTLU teaching innovation competition and executive dean of the Academy of Future Education at Xi'an Jiaotong-Liverpool University.

This final was not the first meeting between Zhang Xiaojun and Zhang Yongce. As early as several years ago, when Zhang Yongce won second prize in the XJTLU teaching innovation competition with a virtual laboratory, Zhang Xiaojun had noticed this unremarkable experiment course teacher.

"He is a 'tech geek.'" Zhang Xiaojun did not hide his appreciation for Zhang Yongce, and said bluntly that currently the teaching innovation of domestic university teachers often focuses on teaching ideas and teaching methods, and their innovation is often individual behavior, but Zhang Yongce shifted the "base" of teaching innovation from some adjustment of teaching ideas to the level of technological innovation.

"This kind of adjustment 'from soft to hard' is harder than teaching innovation in the usual sense and places higher demands on teachers, but if applied properly, it will have a greater promoting effect on teaching," Zhang Xiaojun said with emotion. "It's just that in today's domestic universities, 'tech geeks' like Teacher Zhang are really too few."

If the technological factor is regarded as the "technique" of Zhang Yongce's innovation, his "way" of innovation is even more praised by Zhang Xiaojun.

The XJTLU teaching innovation competition was founded in 2016. As one of the main founders of the competition, over 10 years Zhang Xiaojun has seen tens of thousands of teaching innovators, among whom there is no lack of people who "innovate merely to compete."

"For the competition, spending a few months ahead on a teaching innovation—such an approach cannot be said to be completely without value, but its value is extremely limited," Zhang Xiaojun said. Zhang Yongce, however, is a typical case of "advancing a little every day, combining personal hobbies with work." This can be seen from his years of persistence in improving teaching technology.

In Zhang Xiaojun's view, "this is true teaching innovation."

However, some teachers also raised some doubts about this. For example, at the final site, a judge asked bluntly: "You have already participated in a competition with a technological invention. This time, what do you think is different about your results? What tangible effects has your invention achieved on students?"

"You asked a particularly good question," Zhang Yongce said with a smile.

A competition without prizes

"Many times, technology is just a layer of window paper that breaks with a poke," Zhang Yongce explained to China Science Daily that the core of AI-recognition titration experimental equipment is to combine machine vision, neural networks, and mechanical control with AI assistance. Reaching this step is not particularly difficult; the harder point lies in what goal to set for the device, and under this goal, how the device should control the speed at which it drips solution into the liquid, and when it should "stop." These problems require the operator to combine experimental experience and "tell" the device through code.

"This involves programming, AI training, and other content, and these are exactly what I want students to learn," he told the reporter. At present, in many heavyweight chemical research institutions and laboratories, the combination of AI and chemical experiments is not uncommon, but these devices are often very high-end, and ordinary students have difficulty accessing them. Although the devices he designed are much more "grassroots" and rudimentary, the underlying logic is the same.

More importantly, the manufacturing cost of these devices is not high, and students can use them at any time.

Precisely because of this, after successfully making the first set of equipment, Zhang Yongce's first thought was whether he could use this "hand-built" equipment to organize a "challenge competition" for students.

"The equipment is ready-made, I can open-source the code, and I can teach them how to use it. In this way, students can personally 'get hands-on,'" Zhang Yongce thought it through well, but only five students came to the first information session he organized. When asked why they were not interested in this competition, the students' answers were very consistent: "It's not that we're not interested, it's that we're afraid we can't do it!"

This gave Zhang Yongce a bit of a headache.

"I didn't want students to do 'from 0 to 1' innovation. I have already walked that process for them; what they need to do is just start from '1' and move forward." Realizing that simple information sessions could hardly dispel students' doubts, he began explaining to students one by one to spark their interest. In the end, 19 teams composed of more than 60 students became the first batch of "people who ate crabs."

For a newly founded "grassroots competition," having such a participation scale was already not easy, but Zhang Yongce did not stop there. Because he did not want to limit the competition to the DUT campus. Therefore, shortly after the first challenge competition ended, he began preparing for the second intelligent titration challenge competition at the end of that year.

This time, he wanted to invite teachers and students from other schools to participate.

Hand-building machines is not easy, and organizing competitions is even harder, after all, he is just an ordinary teacher. Fortunately, after many years of teaching, with his helpful personality, he had accumulated good relations "in the circle." So he began calling chemistry teachers at other domestic universities to invite them to participate.

It was also at this time that Liu Hongyun received that phone call "inviting her to compare notes."

Liu Hongyun strongly supported Zhang Yongce's invitation and encouraged her own students to eagerly give it a try. It was under such encouragement that Chu Jingyu, then a sophomore at Beijing Normal University, somewhat bemusedly set foot on this completely unfamiliar track.

As an undergraduate in the chemistry department, Chu Jingyu had hardly encountered AI in her previous studies, and she had never imagined that one day she would become involved with knowledge outside chemistry, such as AI, machine vision, and mechanical control. Therefore, although she signed up under her teacher's encouragement, she still had a question mark in her heart about whether she could really do it.

"Let me give it a try. There's a ready-made machine anyway, and there's nothing to lose if I can't make it work," she thought.

For that challenge competition, Zhang Yongce invited 41 student teams from more than 30 universities across the country to participate, and most of the students shared a mindset similar to Chu Jingyu's.

Looking back now, it was a very rudimentary competition—there were no prizes, students participated voluntarily, they learned coding knowledge through online meetings, several self-made devices were provided for on-campus students to use in turn, while students from other schools had to wait for Zhang Yongce to courier the devices to their schools before they could practice. After they finished using them, they also had to courier the devices to the next university...

"The competition process inevitably involved a lot of rushing about, but after all, we were not just talking on paper," Chu Jingyu said, giving an example: by entering a command on the keyboard, the machine could transfer a fixed volume of liquid. This process would inevitably produce deviations, but how were the deviations produced? How could they be improved to eliminate errors to the greatest extent? How should the code be written to obtain values as precise as possible? "Only in the process of hands-on practice can we truly understand these questions."

"Organizing competitions" was not the only thing Zhang Yongce did that year. He did not want the machines he developed to become merely competition equipment; rather, he wanted them to truly enter the classroom. In fact, shortly after the first on-campus challenge competition ended, he offered a dedicated course in that year's fall semester for students in the university's Strong Foundation Program, and also organized students to participate in that year's National College Student Chemistry Experiment Innovation Design Competition, where they achieved an excellent result by winning the special prize at the national level.

A Valuable "Instinct"

Now, three years have passed, and during this time Zhang Yongce has done many things—in addition to AI-recognition titration experiment equipment, he has designed related AI+chemistry experiment equipment for the four major branches of chemistry (organic chemistry, inorganic chemistry, analytical chemistry, and physical chemistry); in terms of competitions, the series of city competitions he organized rapidly expanded to 13 provinces across the country, with more than 100 universities participating and more than 1,200 students taking part.

More importantly, many schools have already begun to spontaneously organize related courses based on the equipment he provided, and Zhang Yongce did not even know about some of these courses.

"A couple of days ago, I went to a university in another city, originally to help them debug the equipment parameters, but only after arriving did I learn that they had already set up the course," Zhang Yongce said with a smile. "To a certain extent, this shows the urgent demand for 'AI+' in university chemistry experiment teaching."

While Zhang Yongce was busy, others were not idle either.

For example, when she first participated in the intelligent titration challenge competition, Chu Jingyu learned some knowledge related to machine vision. Shortly after the competition ended, she and her classmates received another experiment project. During the project, Chu Jingyu found that some of the experimental content could make use of the AI vision technology she had learned earlier.

The day before she was interviewed by China Science Daily, Chu Jingyu had just participated in an experiment demonstration event. At the event, what they demonstrated was exactly that chemistry experiment that applied machine vision technology.

Liu Hongyun told the reporter that when Chu Jingyu first participated in the intelligent titration challenge competition, she not only had zero exposure to AI technology but also lacked confidence in whether she could learn the relevant technology. But in just over a year, she had already been able to independently propose her own ideas, and even with limited programming ability, she could use AI to turn her ideas into reality.

"For students like them who grew up in traditional chemistry education, this is already quite remarkable," Liu Hongyun said.

In Zhang Xiaojun's view, on today's university campuses, neither "Zhang Yongce" nor "Chu Jingyu" is an isolated case. This is because, with technological progress, the threshold for ordinary people to use AI to achieve breakthroughs in "teaching" and "learning" is lowering. Especially in the past two years, AI-driven zero-code web page generation (Web Coding) technology has become increasingly mature, and even teachers with no coding background at all can generate their own online course platforms. "As far as I know, several university teachers have already achieved good results in this regard."

The development of technology is requiring teachers to proactively integrate AI with professional courses while also lowering the technical threshold for such integration. In Zhang Xiaojun's view, this is the general trend. It is precisely against this background that the practice of Zhang Yongce, who is both "willing" and "good at" integration, takes on an additional layer of distinctive significance.

"As mentioned earlier, the teaching innovation carried out by Teacher Zhang was not deliberate but rather an instinctive behavior naturally arising from long-term teaching," Zhang Xiaojun said frankly. As the technical threshold for teaching innovation continues to lower, teachers' own initiative and proactiveness will become more prominent. This means that in future teaching innovation, "whether teachers are willing" will matter far more than "whether teachers are able."

"Therefore, we need to think more carefully about a question—how can more teachers possess this 'instinct'?" Zhang Xiaojun said.

China Science Daily (November 3, 2026, Page 4, Higher Education Focus) Special Statement: This article is reprinted solely for the purpose of disseminating information and does not mean that it represents the views of this website or confirms the authenticity of its content; if other media, websites, or individuals reprint and use it from this website, they must retain the "source" indicated by this website and bear their own legal liability for copyright and other matters; if the author does not wish for the article to be reprinted or wishes to contact us regarding reprint fees and other matters, please contact us.

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